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A curated list of awesome work on causal inference, particularly in machine learning.

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Awesome-Causal-Inference

A curated list of awesome work on causal inference and (some) causal discovery, particularly in machine learning.

I am gathering resources (currently @ 271 papers) as literature for my PhD, and thought it may come in useful for others. This list includes work relating causal inference to deep learning, statistics, machine learning, and representation learning. If I've missed your paper, or there's a paper you want on the list, then feel free to contribute or email me : ]

The are ordered by year (new to old).

2021

Identification of Latent Variables From Graphical Model Residuals. Hayete, Gruber, Decker, Yan. https://arxiv.org/pdf/2101.02332.pdf

Disentangling Observed Causal Effects from Latent Confounders using Method of Moments. Liu, Liu, Li, Karimi-Bidhendi, Yue, Anandkumar. https://arxiv.org/pdf/2101.06614.pdf

Counterfactual Generative Networks. Sauer, Geiger. https://arxiv.org/pdf/2101.06046.pdf

Model Compression for Domain Adaptation through Causal Effect Estimation. Rotman, Feder, Reichart. https://arxiv.org/pdf/2101.07086.pdf

Discrete Graph Structure Learning for Forecasting Multiple Time Series. Shang, Chen, Bi. https://arxiv.org/pdf/2101.06861.pdf

Instance-Specific Causal Bayesian Network Structure Learning. Jabbari. http://d-scholarship.pitt.edu/40018/19/Jabbari%20Final%20ETD.pdf

The limits of graphical causal discovery. Sevilla. https://towardsdatascience.com/the-limits-of-graphical-causal-discovery-92d92aed54d6?gi=db403d386344

Estimating Average Treatment Effects via Orthogonal Regularization. Hatt, Feuerriegel. https://arxiv.org/pdf/2101.08490.pdf

CDSM--Casual Inference using Deep Bayesian Dynamic Survival Models. Zhu, Gallego. https://arxiv.org/pdf/2101.10643.pdf

Nonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms. Curth, van der Schaar. https://arxiv.org/pdf/2101.10943.pdf

Causality and independence in perfectly adapted dynamical systems. Blom, Mooij. https://arxiv.org/pdf/2101.11885.pdf

Causal inference for quantile treatment effects. Sun, Moodie, Neslehova. https://onlinelibrary.wiley.com/doi/abs/10.1002/env.2668

Variational Bayes survival analysis for unemployment modelling. Boskoski, Perne, Ramesa, Boshkoska. https://arxiv.org/pdf/2102.02295.pdf

A scoping review of causal methods enabling predictions under hypothetical interventions. Lin, Sperrin, Jenkins, Martin, Peek. https://link.springer.com/article/10.1186/s41512-021-00092-9

Estimating the treatment effect for adherers using multiple imputation. Luo, Ruberg, Qu. https://arxiv.org/pdf/2102.03499.pdf

On the Sample Complexity of Causal Discovery and the Value of Domain Expertise. Wadhwa, Dong. https://arxiv.org/pdf/2102.03274.pdf

Integer Programming for Causal Structure Learning in the Presence of Latent Variables. Chen, Dash, Gao. https://arxiv.org/pdf/2102.03129.pdf

Improving Causal Discovery By Optimal Bayesian Network Learning. Lu. Zhang, Yuan. https://www.aaai.org/AAAI21Papers/AAAI-8537.LuN.pdf

Estimating Identifiable Causal Effects through Double Machine Learning. Jung, Tian, Bareinboim. https://www.aaai.org/AAAI21Papers/AAAI-8987.JungY.pdf

Counterfactual Explanation with Multi-Agent Reinforcement Learning for Drug Target Prediction. Nguyen, Quinn, Nguyen, Tran. https://arxiv.org/pdf/2103.12983.pdf

User-oriented smart general AI system under causal inference. Peng. https://arxiv.org/pdf/2103.14561.pdf

A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen. https://arxiv.org/pdf/2103.12206.pdf

Average Treatment Effects in the Presence of Interference. Hu, Li, Wager. https://arxiv.org/pdf/2104.03802.pdf

FRITL: A Hybrid Method for Causal Discovery in the Presence of Latent Confounders. Chen, Zhang, Cai, Huang, Ramsey, Hao, Glymour. https://arxiv.org/pdf/2103.14238.pdf

A brief introduction to causal inference. Nguyen. http://review.ttu.edu.vn/index.php/review/article/download/112/120&hl=en&sa=X&d=3868739188093254491&ei=S3B1YKqkKPqB6rQP7YGXiAI&scisig=AAGBfm2BnYjxbqbGRyi2ySV4VOsV383PQg&nossl=1&oi=scholaralrt&html=&folt=cit

Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters. Fernandez-Loria, Provost. https://arxiv.org/pdf/2104.04103.pdf

Being bayesian about causal inference. Bucur. https://repository.ubn.ru.nl/bitstream/handle/2066/226922/226922.pdf?sequence=1&isAllowed=y

A computational model for complex systems analysis: Causality estimation. Sinha, Loparo. https://www.sciencedirect.com/science/article/abs/pii/S0167278921000737

Post-selection Problems for Causal Inference with Invalid Instruments: A Solution Using Searching and Sampling. Guo. https://arxiv.org/pdf/2104.06911.pdf

On the implied weights of linear regression for causal inference. Chattopadhyay, Zubizarreta. https://arxiv.org/pdf/2104.06581.pdf

Fast and effective pseudo transfer entropy for bivariate data-driven causal inference. Silini, Masoller. https://www.nature.com/articles/s41598-021-87818-3

Shadow-Mapping for Unsupervised Neural Causal Discovery. Vowels, Camgoz, Bowden. https://arxiv.org/pdf/2104.08183.pdf

Sequential Deconfounding for Causal Inference with Unobserved Confounders. Hatt, Feuerriegel. https://arxiv.org/pdf/2104.09323.pdf

CATE meets ML-The Conditional Average Treatment Effect and Machine Learning. Jacob. https://arxiv.org/pdf/2104.09935.pdf

A calculus for causal inference with instrumental variables. Wong. https://arxiv.org/pdf/2104.10633.pdf

Causal-TGAN: Generating Tabular Data Using Causal Generative Adversarial Networks. Wen, Colon, Subbalakshmi, Chandramouli. https://arxiv.org/pdf/2104.10680.pdf

Causal Discovery. Sucar https://link.springer.com/chapter/10.1007/978-3-030-61943-5_15

Understanding the causal structure among the tags in marketing systems. Zheng, Yang, Liu https://link.springer.com/article/10.1007/s00521-020-05552-9

Nonlinear Invariant Risk Minimization: A Causal Approach. Lu.Wu, Hernandez-Lobato, Scholkopf https://arxiv.org/pdf/2102.12353.pdf

Beware of the Simulated DAG! Varsortability in Additive Noise Models. Reisach, Seiler, Weichwald https://arxiv.org/pdf/2102.13647.pdf

Covariate balancing for causal inference on categorical and continuous treatments. Lee, Ma, de Luna https://arxiv.org/pdf/2103.00527.pdf

Toward Causal Representation Learning. Scholkopf, Locatello, Bauer, Ke, Kalchbrenner, Goyal, Bengio https://ieeexplore.ieee.org/document/9363924/?denied=

Improving Causal Inference by Increasing Model Expressiveness. Jensen https://www.aaai.org/AAAI21Papers/SMT-427.JensenD.pdf

A Generative Adversarial Framework for Bounding Confounded Causal Effects. Hu, Wu, Zhang, Wu https://www.aaai.org/AAAI21Papers/AAAI-3651.HuY.pdf

Why did the distribution change? Budhathoki, Janzing, Blobaum, Ng https://arxiv.org/pdf/2102.13384.pdf

Incorporating Causal Graphical Prior Knowledge into Predictive Modeling via Simple Data Augmentation. Teshima, Sugiyama https://arxiv.org/pdf/2103.00136.pdf

Regularizing towards Causal Invariance: Linear Models with Proxies. Oberst, Thams, Peters, Sontag https://arxiv.org/pdf/2103.02477.pdf

Relate and predict: Structure-Aware prediction with Jointly Optimized Neural DAG. Sekhon, Wang, Qi https://arxiv.org/pdf/2103.02405.pdf

Estimating Identifiable Causal Effects on Markov Equivalence Class through Double Machine Learning. Jung, Tian, Bareinboim https://causalai.net/r71.pdf

D?ya like DAGs? A Survey on Structure Learning and Causal Discovery. Vowels, Camgoz, Bowden https://arxiv.org/pdf/2103.02582.pdf

Non-Parametric Methods for Partial Identification of Causal Effects. Zhang, Bareinboim https://causalai.net/r72.pdf

Placebo Tests for Causal Inference. Eggers, Tunon, Dafoe https://pelg.ucsd.edu/Eggers_2021.pdf

Bayesian Doubly Robust Causal Inference via Loss Functions. Luo, Stephens, Graham, McCoy https://arxiv.org/pdf/2103.04086.pdf

Causality indices for bivariate time series data: a comparative review of performance. Edinburgh, Eglen, Ercole https://arxiv.org/pdf/2104.00718.pdf

Doubly robust confidence sequences for sequential causal inference. Waudby-Smith, Arbour, Sinha, Kennedy, Ramdas https://arxiv.org/pdf/2103.06476.pdf

Quantifying Ignorance in Individual-Level Causal-Effect Estimates under Hidden Confounding. Jesson, Mindermann, Gal, Shalit https://arxiv.org/pdf/2103.04850.pdf

A Tree-based Federated Learning Approach for Personalized Treatment Effect Estimation from Heterogeneous Data Sources. Tan, Chang, Tang https://arxiv.org/pdf/2103.06261.pdf

Time-Reversibility, Causality and Compression-Complexity. Kathpalia, Nagaraj.

Identifiability of causal effects with multiple causes and a binary outcome. Kong, Yang, Wang https://academic.oup.com/biomet/advance-article-abstract/doi/10.1093/biomet/asab016/6168988

Three Essays on Model Selection in Time Series Econometrics. Aka https://refubium.fu-berlin.de/handle/fub188/29740

Causal Inference Q-Network: Toward Resilient Reinforcement Learning. Yang, Hung, Ouyang, Chen https://arxiv.org/pdf/2102.09677.pdf

Generating Interpretable Counterfactual Explanations By Implicit Minimisation of Epistemic and Aleatoric Uncertainties. Schut, Key, McGrath, Costabello, Sacaleanu, Corcoran, Gal https://arxiv.org/pdf/2103.08951.pdf

Treatment Effect Estimation using Invariant Risk Minimization. Shah, Ahuja, Shanmugam, Wei, Varshney, Dhurandhar https://arxiv.org/pdf/2103.07788.pdf

VCNet and Functional Targeted Regularization For Learning Causal Effects of Continuous Treatments. Nie, Ye, Liu, Nicolae https://arxiv.org/pdf/2103.07861.pdf

Uplift Modeling: from Causal Inference to Personalization. Teinemaa, Albert, Goldenberg https://www.researchgate.net/profile/Dmitri-Goldenberg/publication/349762341_Uplift_Modeling_from_Causal_Inference_to_Personalization/links/60409ccca6fdcc9c780f8b37/Uplift-Modeling-from-Causal-Inference-to-Personalization.pdf

Higher-Order Orthogonal Causal Learning for Treatment Effect. Huang, Leung, Yan, Wu https://arxiv.org/pdf/2103.11869.pdf

NCoRE: Neural Counterfactual Representation Learning for Combinations of Treatments. Parbhoo, Bauer, Schwab https://arxiv.org/pdf/2103.11175.pdf

Causal Inference under Networked Interference and Intervention Policy Enhancement. Ma, Tresp http://proceedings.mlr.press/v130/ma21c/ma21c.pdf

Causal Modeling with Stochastic Confounders. Vo, Wei, Bergsma, Leong http://proceedings.mlr.press/v130/vinh-vo21a/vinh-vo21a.pdf

ANOCE: analysis of causal effects with multiple mediators via constrained structural learning. Cai, Song, Lu https://openreview.net/pdf/8142413a92e7df5fb79598dee863640346d53f5b.pdf

Causal Inference Under Unmeasured Confounding With Negative Controls: A Minimax Learning Approach. Kallus, Mao, Uehara https://arxiv.org/pdf/2103.14029.pdf

Conditions and Assumptions for Constraint-based Causal Structure Learning. Sadeghi, Soo https://arxiv.org/pdf/2103.13521.pdf

Robust estimation of heterogeneous treatment effects using electronic health record data. Li, Wang, Tu https://onlinelibrary.wiley.com/doi/full/10.1002/sim.8926

Causal Discovery with Bijective Fixed-Cause Functionals. Jalaldoust, Salehkaleybar, Kiyavash. http://jalaldoust.com/docs/CDBFF.pdf

User-Oriented Smart General AI System under Causal Inference. Peng https://arxiv.org/pdf/2103.14561.pdf

Deconfounded Score Method: Scoring DAGs with Dense Unobserved Confounding. Bellot, van der Schaar https://arxiv.org/pdf/2103.15106.pdf

A New Causal Approach to Account for Treatment Switching in Randomized Experiments under a Structural Cumulative Survival Model. Ying, Tchetgen https://arxiv.org/pdf/2103.12206.pdf

SEMgraph: An R Package for Causal Network Analysis of High-Throughput Data with Structural Equation Models. Paluzzi, Grassi https://arxiv.org/pdf/2103.08332.pdf

Bayesian optimal experimental design for inferring causal structure. Zemplenyi, Miller https://arxiv.org/pdf/2103.15229.pdf

Multi-Source Causal Inference Using Control Variates. Guo, Wang, Ding, Wang, Jordan https://arxiv.org/pdf/2103.16689.pdf

Intact-VAE: Estimating Treatment Effects under Unobserved Confounding. Wu, Fukumizu https://arxiv.org/pdf/2101.06662.pdf

2020

The Impact of Time Series Length and Discretization on Longitudinal Causal Estimation Methods. Adams, Saria, Rosenblum. https://arxiv.org/pdf/2011.15099.pdf

algcomparison: Comparing the Performance of Graphical Structure Learning Algorithms with TETRAD. Ramsey, Malinsky, Bui https://www.jmlr.org/papers/volume21/19-773/19-773.pdf

Towards causality-aware predictions in static anticausal machine learning tasks: the linear structural causal model case. Neto https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/23%5CCameraReady%5Ccdml_causality_aware.pdf

Evaluation of Algorithm Selection and Ensemble Methods for Causal Discovery. Saldanha, Cosbey, Ayton, Glenski, Cottam, Shivaram, Jefferson, Hutchinson, Arendt, Volkova https://www.cmu.edu/dietrich/causality/CameraReadys-accepted%20papers/28%5CCameraReady%5CEvaluating_Causal_Ensembles_NeurIPS_CR.pdf

Causal World Models by Unsupervised Deconfounding of Physical Dynamics. Li, Yang, Liu, Chen, Chen, Wang https://arxiv.org/pdf/2012.14228.pdf

Intervention Efficient Algorithms for Approximate Learning of Causal Graphs. Addanki, McGregor, Musco https://arxiv.org/pdf/2012.13976.pdf

Amortized learning of neural causal representations. Ke, Wang, Mitrovic, Szummer, Rezende https://arxiv.org/pdf/2008.09301.pdf

Causal future prediction in a Minkowski space-time. Vlontzos, Rocha, Rueckert, Kainz https://arxiv.org/pdf/2008.09154.pdf

Path dependent structural equation models. Srinivasan, Lee, Ahmidi, Shpitser https://arxiv.org/pdf/2008.10706.pdf

A narrative review of methods for causal inference and associated educational resources. Landsittel, Srivastava, Kropf, Kristin https://journals.lww.com/qmhcjournal/Abstract/2020/10000/A_Narrative_Review_of_Methods_for_Causal_Inference.12.aspx?context=LatestArticles

Targeted VAE: structured inference and targeted learning for causal parameter estimation. Vowels, Camgoz, Bowden https://arxiv.org/pdf/2009.13472.pdf

CASTLE: regularization via auxiliary causal graph discovery. Kyono, Zhang https://arxiv.org/pdf/2009.13180.pdf

Identifying treatment effects under unobserved confounding by causal representation learning. Anonymous https://openreview.net/forum?id=D3TNqCspFpM

Causal discovery for causal bandits utilizing separating sets. de Kroon, Belgrave, Mooij https://arxiv.org/pdf/2009.07916.pdf

Learning DAGs with continuous optimization. Zheng https://www.ml.cmu.edu/research/phd-dissertation-pdfs/thesis-zheng-xun.pdf

Hybridizing machine learning methods and finite mixture models for estimating heterogeneous treatment effects in latent classes. Suk, Kim, Kang https://journals.sagepub.com/doi/abs/10.3102/1076998620951983

Tuning causal discovery algorithms. Biza, Tsamardinos, Triantafillou https://pgm2020.cs.aau.dk/wp-content/uploads/2020/09/biza20.pdf

Debiased machine learning of conditional average treatment effects and other causal functions. Semenova, Chernozhukov https://academic.oup.com/ectj/advance-article-abstract/doi/10.1093/ectj/utaa027/5899048

Estimating individual treatment effects with time-varying confounders. Liu, Yin, Zhang https://arxiv.org/pdf/2008.13620.pdf

Confounding feature acquisition for causal effect estimation. Wang, Yi, Joshi, Ghassemi https://arxiv.org/pdf/2011.08753.pdf

Causal inference methods for combining randomized trials and observational studies: a review. Colnet, Mayer, Chen, Dieng, Li, Varoquaux, Vert, Josse, Yang https://arxiv.org/pdf/2011.08047.pdf

Reconstruction of a directed acyclic graph with intervention. Peng, Shen https://projecteuclid.org/download/pdfview_1/euclid.ejs/1605582080

Debiased Inverse Propensity Score Weighting for Estimation of Average Treatment Effects with High-Dimensional Confounders. Wang, Shah https://arxiv.org/pdf/2011.08661.pdf

A novel method for Causal Structure Discovery from EHR data. Shen, Ma, Vemuri, Castro, Caraballo, Simon https://arxiv.org/pdf/2011.05489.pdf

Teaching deep learning causal effects improves predictive performance. Li, Jia, Yang, Kumar, Steinbach, Simon https://arxiv.org/pdf/2011.05466.pdf

Learning Causal Representations for Robust Domain Adaptation. Yang, Yu, Cao, Liu, Wang, Li https://arxiv.org/pdf/2011.06317.pdf

Learning causal semantic representations for out-of-distribution prediction. Liu, Sun, Wang, Li, Qin, Chen, Liu https://arxiv.org/pdf/2011.01681.pdf

Counterfactual Fairness with disentangled causal effect variational autoencoder. Kim, Shin, Jang, Song, Joo, Kang, Moon https://arxiv.org/pdf/2011.11878.pdf

A systematic review of causal methods enabling predictions under hypothetical interventions. Lin, Sperrin, Jenkins, Martin, Peek https://arxiv.org/pdf/2011.09815.pdf

Efficient permutation discovery in causal DAGs. Squires, Amaniampong, Uhler https://arxiv.org/pdf/2011.03610.pdf

Causality-aware counterfactual confounding adjustment as an alternative to linear residualization in anticausal prediction tasks based on linear learners. Neto https://arxiv.org/pdf/2011.04605.pdf

Conditional independence testing for variable selection and causal inference. Bates https://search.proquest.com/openview/f46a5071aecc21df0cbb3f43d408bcfd/1?pq-origsite=gscholar&cbl=18750&diss=y

Interpretable models for Granger causality using self-explaining neural networks. Marcinkevics, Vogt https://mds.inf.ethz.ch/fileadmin/user_upload/gc_neurips2020_workshop_cr.pdf

High-dimensional feature selection for sample efficient treatment effect estimation. Greenewald, Katz-Rogozhnikov, Shanmugam https://arxiv.org/pdf/2011.01979.pdf

Latent causal invariant model. Sun, Wu, Liu, Zheng, Chen, Qin, Liu https://arxiv.org/pdf/2011.02203.pdf

Applications of common entropy for causal inference. Kocaoglu, Shakkottai, Dimakis, Caramanis, Vishwanath https://proceedings.neurips.cc/paper/2020/file/cae7115f44837c806c9b23ed00a1a28a-Paper.pdf

Entropic causal inference: identifiability and finite sample results. Compton, Kocaoglu, Greenewald, Katz https://proceedings.neurips.cc/paper/2020/file/a979ca2444b34449a2c80b012749e9cd-Paper.pdf

Generalized independent noise condition for estimating latent variable causal graphs. Xie, Cai, Huang, Glymour, Hao, Zhang https://proceedings.neurips.cc/paper/2020/file/aa475604668730af60a0a87cc92604da-Paper.pdf

Bayesian causal structural learning with zero-inflated poisson bayesian networks. Choi, Chapkin, Ni https://proceedings.neurips.cc/paper/2020/file/4175a4b46a45813fccf4bd34c779d817-Paper.pdf

Causal autoregressive flows. Khemakhem, Monti, Leech, Hyvarinen https://arxiv.org/pdf/2011.02268.pdf

Causal variables from reinforcement learning using generalized Bellman equations. Herlau https://arxiv.org/pdf/2010.15745.pdf

Domain adaptation under structural causal models. Chen, Buhlmann https://arxiv.org/pdf/2010.15764.pdf

Causalworld: A robotic manipulation benchmark for causal structure and transfer learning. Ahmed, Trauble, Goyal, Neitz, Bengio, Scholkopf, Bauer, Wuthrich https://arxiv.org/pdf/2010.04296.pdf

Representation learning for treatment effect estimation. Yao https://search.proquest.com/openview/d21f343b17412c1af8099ac93ae92fee/1?pq-origsite=gscholar&cbl=18750&diss=y

Neural additive vector autoregression models for causal discovery in time series data. Bussmann, Nys, Latre https://arxiv.org/pdf/2010.09429.pdf

Causal discovery using compression-complexity measures. SY, Nagaraj https://arxiv.org/pdf/2010.09336.pdf

DAGs with no fears: a closer look at continuous optimization for learning bayesian networks. Wei, Gao, Yu https://arxiv.org/pdf/2010.09133.pdf

Learning robust models using the principle of independent causal mechanisms. Muller, Schmier, Ardizzone, Rother, Kothe https://arxiv.org/pdf/2010.07167.pdf

Double robust representation learning for counterfactual prediction. Zeng, Asaad, Tao, Datta, Carin, Li https://arxiv.org/pdf/2010.07866.pdf

Causal learning with sufficient statistics: an information bottleneck approach. Chicharro, Besserve, Panzeri https://arxiv.org/pdf/2010.05375.pdf

Differentiable causal discovery under unmeasured confounding. Bhattacharya, Nagarajan, Malinsky, Shpitser https://arxiv.org/pdf/2010.06978.pdf

Identifying causal-effect inference failure with uncertainty-aware models. Jesson, Mindermann, Shalit, Gal https://arxiv.org/abs/2007.00163

Causally correct partial models for reinforcement learning. Rezende, Danihelka, Papamakarios, Ke, Jiang, Webever, Gregor, Merzic, Viola, Wang, Mitrovic, Besse, Antonoglou, Buesing https://arxiv.org/pdf/2002.02836v1.pdf

Causal curiosity: RL agents discovering self-supervised experiments for causal representation learning. Sontakke, Mehrjou https://arxiv.org/pdf/2010.03110.pdf

Assessing the fairness of classifiers with collider bias. Xi, Liu, Cheng, Li, Liu, Kang https://arxiv.org/pdf/2010.03933.pdf

Disentangling causal effects for hierarchical reinforcement learning. Corcoll. Vicente https://arxiv.org/pdf/2010.01351.pdf

A new representation learning method for individual treatment effect estimation: split covariate representation network. Liu, Tian, Ji, Zheng http://proceedings.mlr.press/v129/qidong20a/qidong20a.pdf

Personalized estimation and causal inference via deep learning algorithms. Liu https://digitalcommons.library.tmc.edu/cgi/viewcontent.cgi?article=1149&context=uthsph_dissertsopen

Disentangled generative causal representation learning. Shen, Liu, Dong, Lian, Chen, Zhang https://arxiv.org/pdf/2010.02637.pdf

Explaining the efficacy of counterfactually-augmented data. Kaushik, Setlur, Hovy, Lipton https://arxiv.org/pdf/2010.02114.pdf

A new framework for causal discovery. van Leeuwen, DeCaria, Chakaborty, Pulido https://arxiv.org/pdf/2010.02247.pdf

Graphical Granger causality by information-theoretic criteria. Hlavackova-Schindler, Plant http://eprints.cs.univie.ac.at/6518/1/264_paper.pdf

Systematic evaluation of causal discovery in visual model based reinforcement learning anonymous https://openreview.net/pdf/fd60f3b99ed8b26cd60f5f884fe2e6eb7e3ec327.pdf

Long-term effect estimation with surrogate representation. Cheng, Guo, Liu https://arxiv.org/pdf/2008.08236.pdf

Heidegger: Interpretable temporal causal discovery. Mansouri, Arab, Zohrevand, Ester https://dl.acm.org/doi/abs/10.1145/3394486.3403220

Deconfounding and causal regularization for stability and external validity. Buhlmann, Cevid https://arxiv.org/pdf/2008.06234.pdf

A Bayesian nonparametric conditional two-sample test with an application to local causal discovery. Boeken, Mooij https://arxiv.org/pdf/2008.07382.pdf

Semiparametric estimation and inference on structural target functions using machine learning and influence functions. Curth, Alaa, Schaar, https://arxiv.org/pdf/2008.06461.pdf

Estimating causal effects with the neural autoregressive density estimator. Garrido, Borysov, Rich, Pereira https://arxiv.org/pdf/2008.07283.pdf

Reparametrization invariance for non-parametric causal discovery. Jorgensen, Hauberg https://arxiv.org/pdf/2008.05552.pdf

Multivariate counterfactual systems and causal graphical models. Shpitser, Richardson, Robins https://arxiv.org/pdf/2008.06017.pdf

Causal inference on discrete data. Budhathoki https://eda.mmci.uni-saarland.de/pubs/2020/phd-budhathoki.pdf

CRUDS: Counterfactual recourse using disentangled subspaces. Downs, Chu, Yacoby, Doshi-Velez, Pan https://finale.seas.harvard.edu/files/finale/files/cruds-_counterfactual_recourse_using_disentangled_subspaces.pdf

A causal lens for peeking into black box predictive models: predictive model interpretation via causal attribution. Khademi, Honava https://arxiv.org/pdf/2008.00357.pdf

Improving and assessing causal inference algorithms for DAGs. Eigenmann https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/428225/1/DoctoralthesisMarcoEigenmann.pdf

Differentiable causal discovery from interventional data. Brouillard, Lachapelle, Lacoste, Lacoste-Julien, Drouin https://arxiv.org/pdf/2007.01754.pdf

Causal feature selection via orthogonal search. Raj, Bauer, Soleymani, Besserve https://arxiv.org/pdf/2007.02938.pdf

High-recall causal discovery for autocorrelated time series with latent confounders. Gerhardus, Runge https://arxiv.org/pdf/2007.01884.pdf

Adapting text embeddings for causal inference. Veitch, Sridhar, Blei http://www.auai.org/uai2020/proceedings/376_main_paper.pdf

Flagpoles anyone? Causal and explanatory asymmetries. Woodward http://philsci-archive.pitt.edu/17419/1/flagpoles2%20with%20changes%20accepted%207.1.20.pdf

IGNITE: a minimax game toward learning individual treatment effects from networked observational data. Guo, Li, Li, Candan, Raglin, Liu http://www.public.asu.edu/~rguo12/IJCAI20_IGNITE_arxiv.pdf

Counterfactual propagation for semi-supervised individual treatment effect estimation. Harada, Kashima https://arxiv.org/pdf/2005.05099.pdf

Simpson's paradox in COVID-19 case fatality rates: a mediation analysis of age-related causal effects. von Kugelgen, Gresele, Scholkopf https://arxiv.org/abs/2005.07180

Counterfactual confounding adjustment for feature representationas learned by deep models: with an application to image classification tasks. Neto https://arxiv.org/abs/2004.09466

Necessary and sufficient conditions for causal feature selection in time series with latent common causes. Mastakouri, Scholkopf, Janzing https://arxiv.org/pdf/2005.08543.pdf

Achieving causal fairness in machine learning. Wu https://search.proquest.com/openview/5b94283bd4da8edc1b14bff3db4c9e77/1?pq-origsite=gscholar&cbl=18750&diss=y

Efficient intervention design for causal discovery with latents. Addanki, Kasiviswanathan, McGregor, Musco https://arxiv.org/pdf/2005.11736.pdf

CausaLM: Causal model explanation through counterfactual language models. Feder, Oved, Shalit, Reichart https://arxiv.org/pdf/2005.13407.pdf

Bayesian network structure learning with causal effects in the presence of latent variables. Chobtham, Constantinou https://arxiv.org/pdf/2005.14381.pdf

A principled approach to multiple causal inference. Mehta https://static1.squarespace.com/static/577d2a80579fb35f94742dbb/t/5eb43f0b8a1cef37852cc5c3/1588870930090/senior_thesis.pdf

Study causal inference techniques for data-driven personalised decision-making. Dai https://vrs.amsi.org.au/wp-content/uploads/sites/75/2020/01/dai_zhou_vrs-report.pdf

Phenomenal causality and sensory realism. Mding, Bruins, Scholkopf, Berens,Wichmann https://journals.sagepub.com/doi/pdf/10.1177/2041669520927038

Causal inference with deep causal graphs. Parafita, Vitria https://arxiv.org/pdf/2006.08380.pdf

Is independence all your need? On the generalization of representations learned from correlated data. Trauble, Creager, Kilbertus, Goyal, Locatello, Scholkopf, Bauer https://arxiv.org/pdf/2006.07886.pdf

Learning decomposed representation for counterfactual inference. Wu, Kuang, Yuan, Li, Zhou https://arxiv.org/pdf/2006.07040.pdf

Robust recursive partitioning for heterogeneous treatment effects with uncertainty quantification. Lee, Zhang, Zame, et al. https://arxiv.org/pdf/2006.07917.pdf

Learning causal models online. Javed, White, Bengio https://arxiv.org/pdf/2006.07461.pdf

Targeted learning: robust statistics for reproducible research. Coyle, Hejazi et al. https://arxiv.org/pdf/2006.07333.pdf

Supervised whole DAG causal discovery. Li, Xiao, Tian https://arxiv.org/pdf/2006.04697.pdf

Causal discovery from incomplete data using an encoder and reinforcement learning . Huang, Zhu, Holloway, Haidar https://arxiv.org/pdf/2006.05554.pdf

Identifying causal structure in dynamical systems. Baumann, Solowjow, Johansson, Trimpe https://arxiv.org/pdf/2006.03906.pdf

Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder. Qi, Hu, Dong, Fan, Dong, Xiao https://www.sciencedirect.com/science/article/abs/pii/S030626192030636X

tvGP-VAE: tensor-variate gaussian process prior variational autoencoder. Campbell, Lio https://arxiv.org/pdf/2006.04788.pdf

OC-FakeDect: classifying deepfakes using one-class variational autoencoder. Khalid, Woo http://openaccess.thecvf.com/content_CVPRW_2020/papers/w39/Khalid_OC-FakeDect_Classifying_Deepfakes_Using_One-Class_Variational_Autoencoder_CVPRW_2020_paper.pdf

Tuning a variational autoencoder for data accountability problem in the Mars science laboratory ground data system. Lakhmiri, Alimo, Le Digabel https://arxiv.org/pdf/2006.03962.pdf

Counterfactual vision and language learning. Abbasnejad, Teneh, Parvaneh, Shi, Hengel http://openaccess.thecvf.com/content_CVPR_2020/papers/Abbasnejad_Counterfactual_Vision_and_Language_Learning_CVPR_2020_paper.pdf

Invariant risk minimization. Arjovsky, Bottou, Gulrajani, Lopez-Paz https://arxiv.org/pdf/1907.02893.pdf

Amortized causal discovery: learning to infer causal graphs from time-series data. Lowe, Madras, Zemel, Welling https://arxiv.org/abs/2006.10833

Structural autoencoders improve representations for generation and transfer. Leeb, Annadani, Bauer, Scholkopf https://arxiv.org/pdf/2006.07796.pdf

Recurrent independent mechanisms. Goyal, Lamb, Hoffmann, Sodhani, Levine, Bengio, Scholkopf https://arxiv.org/abs/1909.10893

A crash course in good and bad controls. Cinelli, Forney, Pearl https://ftp.cs.ucla.edu/pub/stat_ser/r493.pdf

A ladder of causal distances. Peyrard, West https://arxiv.org/pdf/2005.02480.pdf

Off-the-shelf deep learning is not enough: parsimony, Bayes and causality . Vasudevan, Ziatdinov, Vlcek, Kalinin https://arxiv.org/pdf/2005.01557.pdf

Gradient-based neural DAG learning with interventions. Brouillard, Drouin, Lachapelle, Lacoste, Lacoste-Julien https://causalrlworkshop.github.io/pdf/CLDM_10.pdf

Learning transferable task schemas by representing causal invariances. Madarasz, Behrens https://causalrlworkshop.github.io/pdf/CLDM_25.pdf

Designing data augmentation for simulating interventions. Ilse, Tomczak, Forre https://arxiv.org/pdf/2005.01856.pdf

A causal view n robustness of neural networks. Zhang, Zhang, Li https://arxiv.org/pdf/2005.01095.pdf

Estimation of post-nonlinear causal models using autoencoding structure. Uemura, Shimizu https://ieeexplore.ieee.org/abstract/document/9053468/

Potential outcome and directed acyclic graph approaches to causality: relevance for empirical practice in economics. Imbens https://arxiv.org/abs/1907.07271

Causal inference analysis with neural networks. Alonso https://j1nma.com/documents/JuanManuelAlonso-MTPaper.pdf

A survey on causal inference. Yao, Chu, Li, Li, Gao, Zhang https://arxiv.org/abs/2002.02770

MissDeepCausal: causal inference from incomplete data using deep latent variable models. Mayer, Josse, Raimundo, Vert https://arxiv.org/abs/2002.10837

Accurate data-driven prediction does not mean high reproducibility. Li, Liu, Le, Liu https://www.nature.com/articles/s42256-019-0140-2?proof=t

Causal models for dynamical systems. Peters, Bauer, Pfister https://arxiv.org/pdf/2001.06208.pdf

A critical view of the structural causal model. Galanti, Nabati, Wolf https://arxiv.org/abs/2002.10007

Treatment effect estimation with disentangled latent factors anon https://arxiv.org/abs/2001.10652

CausalVAE: structured causal disentanglement in variational autoencoder. Yang, Liu, Chen, Shen, Hao, Wang https://arxiv.org/pdf/2004.08697.pdf

A robust method for estimating individualized treatment effect. Meng, Qiao https://arxiv.org/pdf/2004.10108.pdf

Dark, beyond deep: a paradigm shift to cognitive AI with humanlike common sense. Zhu, Gao, Fan, Huang, Edmonds, Liu, Gao, Zhang, Qi, Wu, Tenenbaum, Zhu https://www.sciencedirect.com/science/article/pii/S2095809920300345

MultiMBNN: matched and balanced causal inference with neural networks Sharma, Gupta, Prasad, Chatterjee, Vig, Shroff https://arxiv.org/abs/2004.13446

Fairness by learning orthogonal disentangled representations. Sarhan, Navab, Eslami, Albarquouni https://arxiv.org/abs/2003.05707

Bounding causal effects on continuous outcomes. Zhang, Bareinboim https://causalai.net/r61.pdf

2019

Interpretable subgroup discovery in treatment effect estimation with application to opioid prescribing guidelines. Nagpal, Wei, Vinzamuri et al https://arxiv.org/abs/1905.03297

DebFace: De-biasing face recognition, Gong, Liu, Jain https://arxiv.org/abs/1911.08080

Learning individual causal effects from networked observational data. Guo, Li, Liu https://arxiv.org/abs/1906.03485

Machine learning in policy evaluation new tools for causal inference Kreif, DiazOrdaz https://arxiv.org/abs/1903.00402

Counterfactual regression with importance sampling weights. Hassanpour, Greiner https://www.ijcai.org/Proceedings/2019/0815.pdf

The causal structure of suppressor variables. Kim https://journals.sagepub.com/doi/10.3102/1076998619825679

A survey of learning causality with data: problems and methods Guo, Cheng, Li, Hahn, Liu https://arxiv.org/abs/1809.09337

Perfect match: a simple method for learning representations for counterfactual inference with neural networks. Schwab, Linhardt, Karlen https://arxiv.org/abs/1810.00656

Causality matters in medical imaging. Castro, Walker, Glocker https://arxiv.org/abs/1912.08142

Reducing selection bias in counterfactual reasoning for individual treatment effects estimation. Zhang, Lan, Ding, Wang, Hassanpour, Greiner https://arxiv.org/abs/1912.09040

Two causal principles for improving visual dialog. Qi, Niu, Huang, Zhang https://arxiv.org/pdf/1911.10496.pdf

Learning disentangled representations for counterfactual regression. Hassanpour, Greiner https://openreview.net/pdf?id=HkxBJT4YvB

Understanding human judgments of causality. Kazama, Suhara, Bogomolov, Pentland https://arxiv.org/pdf/1912.08998.pdf

Graphical causal models for survey inference Schuessler, Selb https://osf.io/preprints/socarxiv/hbg3m/

Learning counterfactual representations for estimating individual dose-response curves. Schwab, Linhardt, Bauer, Buhmann, Karlen https://arxiv.org/abs/1902.00981

Causal discovery toolbox: uncover causal relationships in Python. Kalainathan, Goudet https://arxiv.org/abs/1903.02278

Causal inference and data-fusion in econometrics. Hunermund, Bareinboim https://arxiv.org/pdf/1912.09104.pdf

Adapting neural networks for the estimation of treatment effects Shi, Blei, Veitch https://arxiv.org/pdf/1906.02120.pdf

Copy, paste, infer: a robust analysis of twin networks for counterfactual inference. Graham, Lee, Perov https://cpb-us-w2.wpmucdn.com/sites.coecis.cornell.edu/dist/a/238/files/2019/12/Id_65_final.pdf

2018

Challenging the hegemony of randomized controlled trails. Pearl https://www.ncbi.nlm.nih.gov/pubmed/29704961

Understanding and misunderstanding randomized controlled trials. Deaton, Cartwright https://www.sciencedirect.com/science/article/pii/S0277953617307359

Using latent variable models to improve causal estimation. Oktay https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2241&context=dissertations_2

Learning representations for counterfactual inference. Johansson, Shalit, Sontag https://arxiv.org/abs/1605.03661

Representation learning for treatment effect estimation from observational data. Yao, Li, Li, Huai, Gao, Zhang https://papers.nips.cc/paper/7529-representation-learning-for-treatment-effect-estimation-from-observational-data.pdf

GANITE: Estimation of individualized treatment effects using generative adversarial nets. Yoon, Jordan, van der Schaar https://openreview.net/forum?id=ByKWUeWA-

Causal reasoning for algorithmic fairness. Loftus, Russell, Kusner, Silva https://arxiv.org/pdf/1805.05859.pdf

Granger-causal attentive mixtures of experts: learning important features with neural networks. Schwab, Miladinovic, Karlen https://arxiv.org/pdf/1802.02195.pdf

Structural causal bandits: where to intervene? Lee, Bareinboim https://causalai.net/r36.pdf

2017

Counterfactual fairness. Kusner, Loftus, Russell, Silva https://papers.nips.cc/paper/6995-counterfactual-fairness

Avoiding discrimination through causal reasoning. Kilbertus, Rojas-Carulla, Parascandolo, Hardt, Janzing, Scholkopf https://arxiv.org/abs/1706.02744

Elements of causal inference. Peters, Janzing, Scholkopf https://mitpress.mit.edu/books/elements-causal-inference

Deep counterfactual networks with propensity-dropout. Alaa, Weisz, van der Schaar https://arxiv.org/abs/1706.05966

Estimating individual treatment effect: generalization bounds and algorithms. Shalit, Johansson, Sontag https://arxiv.org/pdf/1606.03976.pdf

Causal effect inference with deep latent-variable models. Louizos, Shalit, Mooij, Sontag, Zemel, Welling https://arxiv.org/pdf/1705.08821.pdf

When worlds collide: integrating different counterfactual assumptions in fairness. Russell, Kusner, Loftus, Silva https://papers.nips.cc/paper/7220-when-worlds-collide-integrating-different-counterfactual-assumptions-in-fairness.pdf

2016

Recursive partitioning for heterogeneous causal effects. Athey, Imbens https://www.pnas.org/content/pnas/113/27/7353.full.pdf

Double/debiased machine learning for treatment and structural parameters. Chernozhukov, Chetverikov, Demirer, Duflo, Hansen, Newey, Robins https://economics.mit.edu/files/12538

Targeted maximum likelihood estimation for causal inference in observational studies. Schuler, Rose https://academic.oup.com/aje/article/185/1/65/2662306

Causal inference in law: an epidemiological perspective. Siegerink, Hollander, Zeegers, Middelburg https://www.cambridge.org/core/journals/european-journal-of-risk-regulation/article/causal-inference-in-law-an-epidemiological-perspective/316A2F893F03FCA836354BCC21BDC1E4

Equality of opportunity in supervised learning. Hardt, Price, Srebro https://papers.nips.cc/paper/6374-equality-of-opportunity-in-supervised-learning.pdf

2015

Do-calculus when the true graph is unknown. Hyttinen, Eberhardt, Jarvisalo

Exploring causal relationships in visual object tracking. Lebeda, Hadfield, Bowden http://openaccess.thecvf.com/content_iccv_2015/papers/Lebeda_Exploring_Causal_Relationships_ICCV_2015_paper.pdf

Bandits with unobserved confounders: a causal approach. Bareinboim, Forney, Pearl https://ftp.cs.ucla.edu/pub/stat_ser/r460.pdf

2014

Entering the era of data science: targeted learning and the integration of statistics and computational data analysis. van der Laan, Starmans https://www.hindawi.com/journals/as/2014/502678/

Seeing the arrow of time. Pickup, Pan, Wei, Shih, Zhang, Zisserman, Scholkopf, Freeman https://www.robots.ox.ac.uk/~vgg/publications/2014/Pickup14/pickup14.pdf

Causal diagrams for interference. Ogburn, VanderWeele https://arxiv.org/pdf/1403.1239.pdf

Causal models and learning from data: integrating causal modeling and statistical estimation. Petersen, van der Laan https://journals.lww.com/epidem/Fulltext/2014/05000/Causal_Models_and_Learning_from_Data__Integrating.13.aspx

2013

Counterfactual reasoning and learning systems: the example of computational advertising. Bottou, Peters, Quinonero-Candela, Charles, Chickering, Portugaly, Ray, Simard, Snelson https://www.microsoft.com/en-us/research/wp-content/uploads/2013/11/bottou13a.pdf

A sound and complete algorithm for learning causal models from relational data. Maier, Marazopoulou, Arbour, David https://arxiv.org/abs/1309.6843

2012

On a class of bias-amplifying variables that endanger effect estimates. Pearl https://arxiv.org/abs/1203.3503

Quantifying causal influences Janzing, Balduzzi, Grosse-Wentrup, Scholkopf https://arxiv.org/pdf/1203.6502.pdf http://webdav.tuebingen.mpg.de/causality/

Information flows in causal networks. Ay, Polani https://sfi-edu.s3.amazonaws.com/sfi-edu/production/uploads/sfi-com/dev/uploads/filer/45/5f/455fd460-b6b0-4008-9de1-825a5e2b9523/06-05-014.pdf

2011

Separated at birth: statisticians, social scientists, and causality in health services research. Dowd https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3064910/

2010

Proabilistic latent variable models for distinguishing between cause and effect. Mooij, Stegle, Janzing, Zhang, Scholkopf https://papers.nips.cc/paper/4173-probabilistic-latent-variable-models-for-distinguishing-between-cause-and-effect.pdf

2009

In the light of time. Tuisku, Pernu, Annila https://royalsocietypublishing.org/doi/pdf/10.1098/rspa.2008.0494

2005

Causal inference using potential outcomes: design, modeling, decisions. Rubin https://www.jstor.org/stable/2335942

Does matching overcome LaLonde's critique of nonexperimental estimators? Smith, Todd https://www.sciencedirect.com/science/article/abs/pii/S030440760400082X

1983

The central role of the propensity score in observational studies for causal effects. Rosenbaum, Rubin https://www.jstor.org/stable/2335942

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A curated list of awesome work on causal inference, particularly in machine learning.

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