Assessing Treatment Effect Variation in Observational Studies: Results from a Data Challenge
By Carlos M. Carvalho, Avi Feller, Jared S. Murray, Spencer Woody, D. Yeager. Observational Studies, 2019
47 citations recorded by the source corpus.
Abstract:A growing number of methods aim to assess the challenging question of treatment effect variation in observational studies. This special section of Observational Studies reports the results of a workshop conducted at the 2018 Atlantic Causal Inference Conference designed to understand the similarities and differences across these methods. We invited eight groups of researchers to analyze a synthetic observational data set that was generated using a recent large-scale randomized trial in education. Overall, participants employed a diverse set of methods, ranging from matching and flexible outcome modeling to semiparametric estimation and ensemble approaches. While there was broad consensus on the topline estimate, there were also large differences in estimated treatment effect moderation. This highlights the fact that estimating varying treatment effects in observational studies is often more challenging than estimating the average treatment effect alone. We suggest several directions for future work arising from this workshop.
Subfields: Management Science and Operations Research, Statistics and Probability
View Paper: https://www.semanticscholar.org/paper/76855e59d6c8a12194693985a38f461891c2ad8e
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Management Science and Operations Research
In Statistics and Probability
- Lihua Lei
- E.J. Candes
- Martin J. Wainwright
- Cun-Hui Zhang
- Victor Chernozhukov
- Jianqing Fan
- Jinchi Lv
- Peter Buhlmann
Citations in the corpus
- Conformal inference of counterfactuals and individual treatment effects (2020, methodology): “Then we generate [Y (1)] by adding the CATE function (x) (equation (1) of Carvalho et al. (2019)) to m̂0(x).”
- Bayesian Regression Tree Models for Causal Inference: Regularization, Confounding, and Heterogeneous Effects (with Discussion) (2020, unknown): “In this section we react to a few of these after outlining our thinking regarding the design and interpretation of empirical studies (see also Carvalho et al. (2019), where we apply these principles while also calibrating against a real dataset).”