Contributions are welcome. Inspired by GNNpapers.
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Machine learning and causal inference for policy evaluation, KDD, 2015. paper
Susan Athey.
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Generative Learning of Counterfactual for Synthetic Control Applications in Econometrics, arXiv, 2019. paper
Chirag Modi, Uros Seljak.
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Adapting Neural Networks for the Estimation of Treatment Effects, arXiv, 2019. paper code
Claudia Shi, David M. Blei, Victor Veitch.
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RNN-based counterfactual prediction, arXiv, 2019. paper code
Jason Poulos.
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Matrix Completion Methods for Causal Panel Data Models, arXiv, 2018. paper
Susan Athey, Mohsen Bayati, Nikolay Doudchenko, Guido Imbens, Khashayar Khosravi.
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Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks, NIPS, 2018. paper
Bryan Lim, Ahmed Alaa, Mihaela van der Schaar.
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GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets, ICLR, 2018. paper
Jinsung Yoon, James Jordon, Mihaela van der Schaar.
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Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning, arXiv, 2018. paper
Changhee Lee, Nicholas Mastronarde, Mihaela van der Schaar.
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Deep IV: A Flexible Approach for Counterfactual Prediction, PMLR, 2017. paper
Uri Shalit, Fredrik D. Johansson, David Sontag.
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Causal Effect Inference with Deep Latent-Variable Models, arXiv, 2017. paper
Christos Louizos, Uri Shalit, Joris Mooij, David Sontag, Richard Zemel, Max Welling.
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Estimating individual treatment effect: generalization bounds and algorithms, PMLR, 2017. paper code
Uri Shalit, Fredrik D. Johansson, David Sontag.
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Representation Learning for Treatment Effect Estimation from Observational Data, NeurIPS, 2019. paper
Liuyi Yao et al.
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Invariant Models for Causal Transfer Learning, JMLR, 2018. paper
Mateo Rojas-Carulla, Bernhard Schölkopf, Richard Turner, Jonas Peters.
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Learning Representations for Counterfactual Inference, arXiv, 2018. paper code
Fredrik D. Johansson, Uri Shalit, David Sontag.
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Robust Synthetic Control, JMLR, 2019. paper
Muhammad Amjad, Devavrat Shah, Dennis Shen.
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ArCo: An artificial counterfactual approach for high-dimensional panel time-series data, Journal of Econometrics, 2018. paper
Carlos Carvalho, Ricardo Masini, Marcelo C. Medeiros.
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Lasso adjustments of treatment effect estimates in randomized experiments, PNAS, 2016. paper
Adam Bloniarz, Hanzhong Liu, Cun-Hui Zhang, Jasjeet S. Sekhon, Bin Yu.
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Generalized Random Forests, Annals of Statistics, 2019. paper
Susan Athey, Julie Tibshirani, Stefan Wager.
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Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments, NeurIPS, 2019. paper
Vasilis Syrgkanis, Victor Lei, Miruna Oprescu, Maggie Hei, Keith Battocchi, Greg Lewis.
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Orthogonal Random Forest for Causal Inference, PMLR, 2019. paper
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.
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Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning, PNAS, 2019. paper
Miruna Oprescu, Vasilis Syrgkanis, Zhiwei Steven Wu.
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Machine Learning Analysis of Heterogeneity in the Effect of Student Mindset Interventions, Observational Studies, 2019. paper
Fredrik D. Johansson.
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Estimation and Inference of Heterogeneous Treatment Effects using Random Forests, JASA, 2018. paper
Stefan Wager, Susan Athey.
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Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design, PMLR, 2018. paper
Ahmed Alaa, Mihaela Schaar.
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Transfer Learning for Estimating Causal Effects using Neural Networks, arXiv, 2018. paper
Sören R. Künzel, Bradly C. Stadie, Nikita Vemuri, Varsha Ramakrishnan, Jasjeet S. Sekhon, Pieter Abbeel.
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Recursive partitioning for heterogeneous causal effects, PNAS, 2016. paper
Susan Athey, Guido Imbens.
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Machine Learning Methods for Estimating Heterogeneous Causal Effects, ArXiv, 2015. paper
Susan Athey, Guido W. Imbens.
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GP-VAE: Deep Probabilistic Time Series Imputation, arXiv, 2019. paper
Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt.
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NAOMI: Non-Autoregressive Multiresolution Sequence Imputation, arXiv, 2019. paper
Yukai Liu, Rose Yu, Stephan Zheng, Eric Zhan, Yisong Yue.
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GAIN: Missing Data Imputation using Generative Adversarial Nets, ICML, 2018. paper code
Jinsung Yoon, James Jordon, Mihaela van der Schaar.
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MIDA: Multiple Imputation using Denoising Autoencoders, arXiv, 2018. paper code
Lovedeep Gondara, Ke Wang.
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Recurrent Neural Networks for Multivariate Time Series with Missing Values, Scientific Reports, 2018. paper code
Zhengping Che, Sanjay Purushotham, Kyunghyun Cho, David Sontag, Yan Liu.
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BRITS: Bidirectional Recurrent Imputation for Time Series, NeurIPS, 2018. paper
Wei Cao et al.
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Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks, arXiv, 2017. paper code
Jinsung Yoon, William R. Zame, Mihaela van der Schaar.
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Geometric Matrix Completion with Recurrent Multi-Graph Neural Networks, NeurIPS, 2017. paper code
Federico Monti, Michael M. Bronstein, Xavier Bresson.
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Modeling Missing Data in Clinical Time Series with RNNs, JMLR, 2016. paper
Zachary C. Lipton, David C. Kale, Randall Wetzel.
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Sparsity Double Robust Inference of Average Treatment Effects, arXiv, 2019. paper
Jelena Bradic, Stefan Wager, Yinchu Zhu.
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Time Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders, arXiv, 2019. paper
Ioana Bica, Ahmed M. Alaa, Mihaela van der Schaar.
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Deep Neural Networks for Estimation and Inference, arXiv, 2019. paper
Max H. Farrell, Tengyuan Liang, Sanjog Misra.
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Approximate Residual Balancing: De-Biased Inference of Average Treatment Effects in High Dimensions, JRSS-B, 2018. paper
Susan Athey, Guido W. Imbens, Stefan Wager.
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Deep Counterfactual Networks with Propensity-Dropout, arXiv, 2017. paper
Ahmed M. Alaa, Michael Weisz, Mihaela van der Schaar.
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Double/Debiased Machine Learning for Treatment and Causal Parameters, arXiv, 2017. paper
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, James Robins.
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Doubly Robust Policy Evaluation and Optimization, Statistical Science, 2014. paper
Miroslav Dudík, Dumitru Erhan, John Langford, Lihong Li.
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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms, arXiv, 2019. paper
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal.
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Causal Discovery with Reinforcement Learning, arXiv, 2019. paper
Shengyu Zhu, Zhitang Chen.
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Adversarial Generalized Method of Moments, arXiv, 2019. paper code
Greg Lewis, Vasilis Syrgkanis.
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CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training, arXiv, 2019. paper
Murat Kocaoglu, Christopher Snyder, Alexandros G. Dimakis, Sriram Vishwanath.
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Learning When-to-Treat Policies, arXiv, 2019. paper
Xinkun Nie, Emma Brunskill, Stefan Wager.
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Learning Neural Causal Models from Unknown Interventions, arXiv, 2019. paper code
Nan Rosemary Ke, Olexa Bilaniuk, Anirudh Goyal, Stefan Bauer, Hugo Larochelle, Chris Pal, Yoshua Bengio.
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Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks, ICML, 2018. paper
Onur Atan, William R. Zame, Mihaela van der Schaar.
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Causal Bandits: Learning Good Interventions via Causal Inference, NIPS, 2016. paper
Finnian Lattimore, Tor Lattimore, Mark D. Reid.
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Counterfactual Risk Minimization: Learning from Logged Bandit Feedback, arXiv, 2015. paper
Adith Swaminathan, Thorsten Joachims.
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The Deconfounded Recommender: A Causal Inference Approach to Recommendation, arXiv, 2019. paper code
Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei.
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The Blessings of Multiple Causes, arXiv, 2019. paper
Yixin Wang, David M. Blei.
comments
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Comment: Reflections on the Deconfounder, arXiv, 2019. paper
Alexander D'Amour
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On Multi-Cause Causal Inference with Unobserved Confounding: Counterexamples, Impossibility, and Alternatives, arXiv, 2019. paper
Alexander D'Amour
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Comment on "Blessings of Multiple Causes", arXiv, 2019. paper
Elizabeth L. Ogburn, Ilya Shpitser, Eric J. Tchetgen Tchetgen.
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The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019), arXiv, 2019. paper
Yixin Wang, David M. Blei.
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Recommendations as Treatments: Debiasing Learning and Evaluation, PMLR, 2016. paper
Tobias Schnabel, Adith Swaminathan, Ashudeep Singh, Navin Chandak, Thorsten Joachims.
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Collaborative Prediction and Ranking with Non-Random Missing Data, RecSys, 2009. paper
Benjamin M. Marlin, Richard S. Zemel.
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State-Building through Public Land Disposal? An Application of Matrix Completion for Counterfactual Prediction, arXiv, 2019. paper code
Jason Poulos.
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Estimating Treatment Effects with Causal Forests: An Application, arXiv, 2019. paper
Susan Athey, Stefan Wager.
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Ensemble Methods for Causal Effects in Panel Data Settings, AER P&P, 2019. paper
Susan Athey, Mohsen Bayati, Guido W. Imbens, Zhaonan Qu.
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Using Text Embeddings for Causal Inference, arXIv, 2019. paper code
Victor Veitch, Dhanya Sridhar, David M. Blei.
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Counterfactual Story Reasoning and Generation, arXIv, 2019. paper
Lianhui Qin, Antoine Bosselut, Ari Holtzman, Chandra Bhagavatula, Elizabeth Clark, Yejin Choi.
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How to Make Causal Inferences Using Texts, arXIv, 2018. paper
Naoki Egami, Christian J. Fong, Justin Grimmer, Margaret E. Roberts, Brandon M. Stewart.
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NeurIPS 2019 Workshop link
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NIPS 2018 Workshop link
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NIPS 2017 Workshop links
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NIPS 2016 Workshop link
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NIPS 2013 Workshop link
- PMLR, Volume 6: Causality: Objectives and Assessment, 12 December 2008, Whistler, Canada link
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EconML: A Python Package for ML-Based Heterogeneous Treatment Effects Estimation link
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Uplift modeling and causal inference with machine learning algorithms link
- CS7792 - Counterfactual Machine Learning link
- Causality and Machine Learning: Microsoft Research [link] https://www.microsoft.com/en-us/research/group/causal-inference/#!publications