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ML is playing an increasingly crucial role in estimating causal effects of treatments on outcomes from observational data.
Estimating causal effects of treatments in randomized and nonrandomized studies
Donald B. Rubin · 1939
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The central role of the propensity score in observational studies for causal effects
Paul R. Rosenbaum and Donald B. Rubin · 1983
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Evaluating the Econometric Evaluations of Training Programs with Experimental Data
Robert J. LaLonde · 1986
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Effects of early intervention on cognitive function of low birth weight preterm infants
J. Brooks-Gunn, F. R. Liaw, and P. K. Klebanov · 1992
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Estimation of Regression Coefficients When Some Regressors are not Always Observed
James M. Robins, Andrea Rotnitzky, and Lue Ping Zhao · 1994
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A study of cross-validation and bootstrap for accuracy estimation and model selection
Ron Kohavi · 1995
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Propensity Score-Matching Methods For Nonexperimental Causal Studies
Rajeev H. Dehejia and Sadek Wahba · 2002
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Efficient Estimation of Average Treatment Effects Using the Estimated Propensity Score
Keisuke Hirano, Guido W. Imbens, and Geert Ridder · 2003
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Does matching overcome LaLonde’s critique of nonexperimental estimators?
Jeffrey A. Smith and Petra E. Todd · 2005
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The Costs of Low Birth Weight
Douglas Almond, Kenneth Y. Chay, and David S. Lee · 2005
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Targeted Maximum Likelihood Learning
Mark J. van der Laan and Daniel Rubin · 2006
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Causality
Judea Pearl · 2009
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Bayesian Nonparametric Modeling for Causal Inference
Jennifer L. Hill · 2010
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Model selection for estimating treatment effects
Craig A. Rolling and Yuhong Yang · 2014
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Learning representations for counterfactual inference
Fredrik D. Johansson, Uri Shalit, and David Sontag · 2016
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LightGBM: A Highly Efficient Gradient Boosting Decision Tree
Guolin Ke, Qi Meng, Thomas Finley, Taifeng Wang, Wei Chen, Weidong Ma, Qiwei Ye, and Tie-Yan Liu · 2017
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Adam: A Method for Stochastic Optimization
Diederik P. Kingma and Jimmy Ba · 2017
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Causal Effect Inference with Deep Latent-Variable Models
Christos Louizos, Uri Shalit, Joris M. Mooij, David Sontag, Richard Zemel, and Max Welling · 2017
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Synth-Validation: Selecting the Best Causal Inference Method for a Given Dataset, October 2017
Alejandro Schuler, Ken Jung, Robert Tibshirani, Trevor Hastie, and Nigam Shah · 2017
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Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning
Sören R. Künzel, Jasjeet S. Sekhon, Peter J. Bickel, and Bin Yu · 2019
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Adapting Neural Networks for the Estimation of Treatment Effects
Claudia Shi, David Blei, and Victor Veitch · 2019
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An Evaluation Toolkit to Guide Model Selection and Cohort Definition in Causal Inference, June 2019
Yishai Shimoni, Ehud Karavani, Sivan Ravid, Peter Bak, Tan Hung Ng, Sharon Hensley Alford, Denise Meade, and Yaara Goldschmidt · 2019
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Using Wasserstein Generative Adversarial Networks for the Design of Monte Carlo Simulations
Susan Athey, Guido Imbens, Jonas Metzger, and Evan Munro · 2020
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Orthogonal Statistical Learning
Dylan J. Foster and Vasilis Syrgkanis · 2020
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Uri Shalit, Fredrik D. Johansson, and David Sontag · 2017
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Double/debiased machine learning for treatment and structural parameters
Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins · 2018
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CatBoost: Unbiased boosting with categorical features
Liudmila Prokhorenkova, Gleb Gusev, Aleksandr Vorobev, Anna Veronika Dorogush, and Andrey Gulin · 2018
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A comparison of methods for model selection when estimating individual treatment effects
Alejandro Schuler, Michael Baiocchi, Robert Tibshirani, and Nigam Shah · 2018
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Representation learning for treatment effect estimation from observational data
Liuyi Yao, Sheng Li, Yaliang Li, Mengdi Huai, Jing Gao, and Aidong Zhang · 2018
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GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets
Jinsung Yoon, James Jordon, and Mihaela van der Schaar · 2018
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Validating Causal Inference Models via Influence Functions
Ahmed Alaa and Mihaela Van Der Schaar · 2019
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A Survey of Learning Causality with Data: Problems and Methods
Ruocheng Guo, Lu Cheng, Jundong Li, P. Richard Hahn, and Huan Liu · 2020
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Counterfactual Cross-Validation: Stable Model Selection Procedure for Causal Inference Models
Yuta Saito and Shota Yasui · 2020
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Liuyi Yao, Zhixuan Chu, Sheng Li, Yaliang Li, Jing Gao, and Aidong Zhang · 2020
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Causal Decision Making and Causal Effect Estimation Are Not the Same… and Why It Matters
Carlos Fernández-Loría and Foster Provost · 2021
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RealCause: Realistic Causal Inference Benchmarking
Brady Neal, Chin-Wei Huang, and Sunand Raghupathi · 2021
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Quasi-oracle estimation of heterogeneous treatment effects
X Nie and S Wager · 2021
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Undersmoothing Causal Estimators with Generative Trees
Damian Machlanski, Spyros Samothrakis, and Paul Clarke · 2022
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Empirical Analysis of Model Selection for Heterogenous Causal Effect Estimation, November 2022
Divyat Mahajan, Ioannis Mitliagkas, Brady Neal, and Vasilis Syrgkanis · 2022
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Grokking-like effects in counterfactual inference
Spyridon Samothrakis, Ana Matran-Fernandez, Umar Abdullahi, Michael Fairbank, and Maria Fasli · 2022
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