Recursive partitioning for heterogeneous causal effects
Susan Athey,Guido W. Imbens +1 more
Reads0
Chats0
TLDR
This paper provides a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects, and proposes an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation.Abstract:
In this paper we propose methods for estimating heterogeneity in causal effects in experimental and observational studies and for conducting hypothesis tests about the magnitude of differences in treatment effects across subsets of the population. We provide a data-driven approach to partition the data into subpopulations that differ in the magnitude of their treatment effects. The approach enables the construction of valid confidence intervals for treatment effects, even with many covariates relative to the sample size, and without “sparsity” assumptions. We propose an “honest” approach to estimation, whereby one sample is used to construct the partition and another to estimate treatment effects for each subpopulation. Our approach builds on regression tree methods, modified to optimize for goodness of fit in treatment effects and to account for honest estimation. Our model selection criterion anticipates that bias will be eliminated by honest estimation and also accounts for the effect of making additional splits on the variance of treatment effect estimates within each subpopulation. We address the challenge that the “ground truth” for a causal effect is not observed for any individual unit, so that standard approaches to cross-validation must be modified. Through a simulation study, we show that for our preferred method honest estimation results in nominal coverage for 90% confidence intervals, whereas coverage ranges between 74% and 84% for nonhonest approaches. Honest estimation requires estimating the model with a smaller sample size; the cost in terms of mean squared error of treatment effects for our preferred method ranges between 7–22%.read more
Citations
More filters
Journal ArticleDOI
Targeted therapy using polymyxin B hemadsorption in patients with sepsis: a post-hoc analysis of the JSEPTIC-DIC study and the EUPHRATES trial
Itsuki Osawa,Tadahiro Goto,Daisuke Kudo,Mineji Hayakawa,Kazuma Yamakawa,Shigeki Kushimoto,Debra Foster,John A. Kellum,Kent Doi +8 more
TL;DR: In this article , the authors applied the machine learning-based causal forest model to the JSEPTIC-DIC cohort to investigate heterogeneity in treatment effects of polymyxin B hemadsorption on 28-day survival after adjusting for potential confounders and ascertain the best criteria for PMX-HA use.
Journal ArticleDOI
Characteristics, Prognosis, and Competing Risk Nomograms of Cutaneous Malignant Melanoma: Evidence for Pigmentary Disorders
Zichao Li,Xinrui Li,Xiaowei Yi,Tianxi Li,Xingning Huang,Xiaoya Ren,T. Moa,Kun Li,Hanfeng Guo,Shengxiu Chen,Yao Ma,Lei Shang,Baoqiang Song,Dahai Hu +13 more
TL;DR: Patients with different types of CMM had different prognostic characteristics and different risk of cause-specific death, enabling targeted intervention in the early period at both the population and individual levels.
Proceedings ArticleDOI
Treatment Effect Estimation Using Invariant Risk Minimization
TL;DR: This article proposed a domain generalization framework of invariant risk minimization (IRM) to estimate causal individual treatment effect (ITE) from observational data, which is a challenging problem whose difficulty is exacerbated by the presence of treatment assignment bias.
Posted Content
Learning and Testing Sub-groups with Heterogeneous Treatment Effects:A Sequence of Two Studies.
TL;DR: This paper proposes a two-study approach to first propose and then test heterogeneous treatment effects, and compares the methods' performance to other state-of-the-art methods in the literature that make use only of the Study 2 data.
Book ChapterDOI
Feature Selection Methods for Uplift Modeling and Heterogeneous Treatment Effect
Totte Harinen,Akra Chowchong +1 more
TL;DR: In this paper , a set of feature selection methods explicitly designed for uplift modeling is presented, drawing inspiration from statistics and information theory, and empirically evaluated on publicly available datasets, demonstrating the advantages of the proposed methods compared to traditional feature selection.
References
More filters
Journal ArticleDOI
Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
Journal ArticleDOI
Regression Shrinkage and Selection via the Lasso
TL;DR: A new method for estimation in linear models called the lasso, which minimizes the residual sum of squares subject to the sum of the absolute value of the coefficients being less than a constant, is proposed.
Book
The Nature of Statistical Learning Theory
TL;DR: Setting of the learning problem consistency of learning processes bounds on the rate of convergence ofLearning processes controlling the generalization ability of learning process constructing learning algorithms what is important in learning theory?
Statistical learning theory
TL;DR: Presenting a method for determining the necessary and sufficient conditions for consistency of learning process, the author covers function estimates from small data pools, applying these estimations to real-life problems, and much more.
Journal ArticleDOI
The central role of the propensity score in observational studies for causal effects
TL;DR: The authors discusses the central role of propensity scores and balancing scores in the analysis of observational studies and shows that adjustment for the scalar propensity score is sufficient to remove bias due to all observed covariates.
Related Papers (5)
Estimation and Inference of Heterogeneous Treatment Effects using Random Forests
Stefan Wager,Susan Athey +1 more