A comparison of methods for model selection when estimating individual
treatment effects

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A comparison of methods for model selection when estimating individual treatment effects

14 April 2018

Alejandro Schuler

Robert Tibshirani

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Papers citing "A comparison of methods for model selection when estimating individual treatment effects"

19 / 19 papers shown

Title
Honesty in Causal Forests: When It Helps and When It Hurts Yanfang Hou Carlos Fernández-Loría 17 0 0 16 Jun 2025
CausalPFN: Amortized Causal Effect Estimation via In-Context Learning Vahid Balazadeh Hamidreza Kamkari Valentin Thomas Benson Li Junwei Ma Jesse C. Cresswell Rahul G. Krishnan CML 17 0 0 09 Jun 2025
Causal Q-Aggregation for CATE Model Selection Hui Lan Vasilis Syrgkanis CML 107 4 0 25 Oct 2023
Causal Dynamic Variational Autoencoder for Counterfactual Regression in Longitudinal Data Mouad El Bouchattaoui Myriam Tami Benoit Lepetit P. Cournède CML OOD 212 2 0 16 Oct 2023
Data-Driven Estimation of Heterogeneous Treatment Effects Christopher Tran Keith Burghardt Kristina Lerman Elena Zheleva CML 56 1 0 16 Jan 2023
Orthogonal Series Estimation for the Ratio of Conditional Expectation Functions Kazuhiko Shinoda T. Hoshino CML 66 0 0 26 Dec 2022
Out-of-sample scoring and automatic selection of causal estimators E. Kraev Timo Flesch H. Lekunze Mark Harley Pere P. Morell CML 13 1 0 20 Dec 2022
Efficient Heterogeneous Treatment Effect Estimation With Multiple Experiments and Multiple Outcomes Leon Yao Caroline Lo Israel Nir S. Tan Ariel Evnine Adam Lerer A. Peysakhovich CML 57 7 0 10 Jun 2022
Meta-Learners for Estimation of Causal Effects: Finite Sample Cross-Fit Performance Gabriel Okasa CML 51 6 0 30 Jan 2022
Meta-Analysis of Randomized Experiments with Applications to Heavy-Tailed Response Data Nilesh Tripuraneni Dhruv Madeka Dean Phillips Foster Dominique C. Perrault-Joncas Michael I. Jordan 69 5 0 14 Dec 2021
A pragmatic approach to estimating average treatment effects from EHR data: the effect of prone positioning on mechanically ventilated COVID-19 patients A. Izdebski P. Thoral R. Lalisang Dean McHugh D. Gommers ... Rutger van Raalte M. V. Tellingen Niels C. Gritters van den Oever Paul Elbers Giovanni Cina CML 48 0 0 14 Sep 2021
DoWhy: Addressing Challenges in Expressing and Validating Causal Assumptions Amit Sharma Vasilis Syrgkanis Cheng Zhang Emre Kıcıman 75 26 0 27 Aug 2021
Causal Decision Making and Causal Effect Estimation Are Not the Same... and Why It Matters Carlos Fernández-Loría F. Provost CML 67 45 0 08 Apr 2021
Shrinkage Bayesian Causal Forests for Heterogeneous Treatment Effects Estimation A. Caron G. Baio I. Manolopoulou CML 84 16 0 12 Feb 2021
Estimating Individual Treatment Effects using Non-Parametric Regression Models: a Review A. Caron G. Baio I. Manolopoulou CML 109 56 0 14 Sep 2020
Stable discovery of interpretable subgroups via calibration in causal studies Raaz Dwivedi Yan Shuo Tan Briton Park Mian Wei Kevin Horgan D. Madigan Bin Yu CML 46 30 0 23 Aug 2020
Learning Counterfactual Representations for Estimating Individual Dose-Response Curves Patrick Schwab Lorenz Linhardt Stefan Bauer J. M. Buhmann W. Karlen CML OOD 101 135 0 03 Feb 2019
Perfect Match: A Simple Method for Learning Representations For Counterfactual Inference With Neural Networks Patrick Schwab Lorenz Linhardt W. Karlen CML BDL 102 111 0 01 Oct 2018
Automated versus do-it-yourself methods for causal inference: Lessons learned from a data analysis competition Vincent Dorie J. Hill Uri Shalit M. Scott D. Cervone CML 283 288 0 09 Jul 2017