Richard Post, Isabel van den Heuvel, Marko Petkovic, Edwin van den Heuvel
arXiv 29 Oct 2022 · Statistics — Methodology · publishedEpidemiology (2023) · 4 citations (OpenAlex)
arXiv:2210.16547 · PDF · DOI · OpenAlex · Extracted main text
Causal inference from observational data requires untestable identification assumptions. If these assumptions apply, machine learning (ML) methods can be used to study complex forms of causal effect heterogeneity. Recently, several ML methods were developed to estimate the conditional average treatment effect (CATE). If the features at hand cannot explain all heterogeneity, the individual treatment effects (ITEs) can seriously deviate from the CATE. In this work, we demonstrate how the distributions of the ITE and the CATE can differ when a causal random forest (CRF) is applied. We extend the CRF to estimate the difference in conditional variance between treated and controls. If the ITE distribution equals the CATE distribution, this estimated difference in variance should be small. If they differ, an additional causal assumption is necessary to quantify the heterogeneity not captured by the CATE distribution. The conditional variance of the ITE can be identified when the individual effect is independent of the outcome under no treatment given the measured features. Then, in the cases where the ITE and CATE distributions differ, the extended CRF can appropriately estimate the variance of the ITE distribution while the CRF fails to do so.
appendix boundary found by appendix_command · 59% of the source is main text. Read the extracted text to check this.
The works this paper leans on most, across its whole bibliography — not restricted to papers in our corpus. Ranked by composite intensity, which combines how often a work is mentioned, how many sections mention it, and how much of that falls in the main text rather than the appendix.
| Reference | Intensity | Mentions | Sections | Main text | |
|---|---|---|---|---|---|
| 1 | Athey S, Tibshirani J, Wager S (2019) Generalized random forests | 1.000 | 5 | 3 | 100% |
| 2 | Hernán MA, Robins JM (2020) Causal Inference: What If | 0.874 | 5 | 2 | 100% |
| 3 | Athey S, Imbens GW (2016) Recursive partitioning for heterogeneous causal effects | 0.811 | 4 | 2 | 100% |
| 4 | Wager S, Athey S (2018) Estimation and Inference of Heterogeneous Treatment Effects using Random Forests | 0.644 | 4 | 1 | 100% |
| 5 | Chiu LS, Pedley A, Massaro JM, Benjamin EJ, Mitchell GF, McManus DD,… (2020) The association of non-alcoholic fatty liver disease and cardiac structure and function—framingham heart study | 0.644 | 3 | 2 | 67% |
| 6 | Naimi AI, Mishler AE, Kennedy EH (2021) Challenges in Obtaining Valid Causal Effect Estimates with Machine Learning Algorithms | 0.644 | 2 | 2 | 100% |
| 7 | Nie X, Wager S (2020) Quasi-oracle estimation of heterogeneous treatment effects | 0.644 | 2 | 2 | 100% |
| 8 | Chernozhukov V, Chetverikov D, Demirer M, Duflo E, Hansen C, Newey W… (2018) Double/debiased machine learning for treatment and structural parameters | 0.511 | 2 | 1 | 100% |
| 9 | Dickerman BA, Hernán MA (2020) Counterfactual prediction is not only for causal inference | 0.511 | 2 | 1 | 100% |
| 10 | Hill JL (2011) Bayesian nonparametric modeling for causal inference | 0.511 | 2 | 1 | 100% |
Showing the top 10 of 57 scored citations.