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Estimating Potential Outcome Distributions with Collaborating Causal Networks

4 October 2021

David Carlson

Papers citing "Estimating Potential Outcome Distributions with Collaborating Causal Networks"

25 / 25 papers shown

Title
Conformal Convolution and Monte Carlo Meta-learners for Predictive Inference of Individual Treatment Effects Jef Jonkers Jarne Verhaeghe Glenn Van Wallendael Luc Duchateau Sofie Van Hoecke 413 2 0 07 Feb 2024
Variational Auto-Encoder Architectures that Excel at Causal Inference Negar Hassanpour Russell Greiner BDL CML 47 3 0 11 Nov 2021
On the Distinction Between "Conditional Average Treatment Effects" (CATE) and "Individual Treatment Effects" (ITE) Under Ignorability Assumptions Brian G. Vegetabile CML 24 20 0 10 Aug 2021
Universal Off-Policy Evaluation Yash Chandak S. Niekum Bruno C. da Silva Erik Learned-Miller Emma Brunskill Philip S. Thomas OffRL ELM 77 53 0 26 Apr 2021
Conditional Distributional Treatment Effect with Kernel Conditional Mean Embeddings and U-Statistic Regression Junhyung Park Uri Shalit Bernhard Schölkopf Krikamol Muandet CML 100 33 0 16 Feb 2021
Counterfactual Representation Learning with Balancing Weights Serge Assaad Shuxi Zeng Chenyang Tao Shounak Datta Nikhil Mehta Ricardo Henao Fan Li Lawrence Carin CML OOD 133 64 0 23 Oct 2020
Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models Andrew Jesson Sören Mindermann Uri Shalit Y. Gal CML 50 74 0 01 Jul 2020
Conformal Inference of Counterfactuals and Individual Treatment Effects Lihua Lei Emmanuel J. Candès CML 170 195 0 11 Jun 2020
Estimating Uncertainty Intervals from Collaborating Networks Tianhui Zhou Yitong Li Yuan Wu David Carlson UQCV 165 16 0 12 Feb 2020
Generalization Bounds and Representation Learning for Estimation of Potential Outcomes and Causal Effects Fredrik D. Johansson Uri Shalit Nathan Kallus David Sontag CML OOD 86 100 0 21 Jan 2020
Adapting Neural Networks for the Estimation of Treatment Effects Claudia Shi David M. Blei Victor Veitch CML 148 376 0 05 Jun 2019
Adversarial Balancing-based Representation Learning for Causal Effect Inference with Observational Data Xin Du Lei Sun W. Duivesteijn Alexander G. Nikolaev Mykola Pechenizkiy OOD CML 48 43 0 30 Apr 2019
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks Patrick Schwab Lorenz Linhardt W. Karlen CML BDL 57 111 0 01 Oct 2018
Confounding-Robust Policy Improvement Nathan Kallus Angela Zhou CML OffRL 332 153 0 22 May 2018
Deep Counterfactual Networks with Propensity-Dropout Ahmed Alaa M. Weisz M. Schaar CML OOD BDL 44 86 0 19 Jun 2017
Meta-learners for Estimating Heterogeneous Treatment Effects using Machine Learning Sören R. Künzel Jasjeet Sekhon Peter J. Bickel Bin Yu CML 164 928 0 12 Jun 2017
Causal Effect Inference with Deep Latent-Variable Models Christos Louizos Uri Shalit Joris Mooij David Sontag R. Zemel Max Welling CML BDL 211 747 0 24 May 2017
Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes Ahmed Alaa M. Schaar CML 173 304 0 10 Apr 2017
Learning Representations for Counterfactual Inference Fredrik D. Johansson Uri Shalit David Sontag CML OOD BDL 288 729 0 12 May 2016
Data-Efficient Off-Policy Policy Evaluation for Reinforcement Learning Philip S. Thomas Emma Brunskill OffRL 432 577 0 04 Apr 2016
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests Stefan Wager Susan Athey SyDa CML 368 2,490 0 14 Oct 2015
Recursive Partitioning for Heterogeneous Causal Effects Susan Athey Guido Imbens CML 262 1,436 0 05 Apr 2015
Performance guarantees for individualized treatment rules Min Qian Susan Murphy 319 558 0 17 May 2011
The Offset Tree for Learning with Partial Labels A. Beygelzimer John Langford 317 185 0 21 Dec 2008
BART: Bayesian additive regression trees H. Chipman E. George R. McCulloch 158 1,795 0 19 Jun 2008