How is recursive partitioning used for heterogeneous causal effects?
Recursive Partitioning for Heterogeneous Causal Effects. In this paper we study the problems of estimating heterogeneity in causal effects in experimental or observational studies and conducting inference about the magnitude of the differences in treatment effects across subsets of the population. In applications, our method provides
How is partitioning used in the causal inference literature?
Conditional Average Treatment Effects and Partitioning. Define the conditional average treatment effect τ(x) ≡ E[Yi(1) − Yi(0)|Xi = x]. A large part of the causal inference literature (e.g., refs. 3 and 12 ⇓ – 14) is focused on estimating the population (marginal) average treatment effect E[Yi(1) − Yi(0)].
Why do we have biases in recursive partitioning?
This is because the methods are “adaptive”: They use the training data for model selection, so that spurious correlations between covariates and outcomes affect the selected model, leading to biases that disappear only slowly as the sample size grows.
How to estimate the magnitude of heterogeneous causal effects?
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.
Which is the ultimate goal in the application of partitioning?
Whether the ultimate goal in an application is to derive a partition or fully personalized treatment effect estimates depends on the setting; settings where partitions may be desirable include those where decision rules must be remembered, applied, or interpreted by human beings or computers with limited processing power or memory.
Which is the best paper for causal inference?
A large part of the causal inference literature (e.g., refs. 3 and 12 ⇓ – 14) is focused on estimating the population (marginal) average treatment effect E[Yi(1) − Yi(0)]. The main focus of the current paper is on obtaining accurate estimates of and inferences for the conditional average treatment effect τ(x).