Michael Lechner, Jana Mareckova
arXiv 16 May 2024 · Econometrics · 1 citations (OpenAlex)
arXiv:2405.10198 · PDF · DOI · OpenAlex · Extracted main text
Uncovering causal effects in multiple treatment setting at various levels of granularity provides substantial value to decision makers. Comprehensive machine learning approaches to causal effect estimation allow to use a single causal machine learning approach for estimation and inference of causal mean effects for all levels of granularity. Focusing on selection-on-observables, this paper compares three such approaches, the modified causal forest (mcf), the generalized random forest (grf), and double machine learning (dml). It also compares the theoretical properties of the approaches and provides proven theoretical guarantees for the mcf. The findings indicate that dml-based methods excel for average treatment effects at the population level (ATE) and group level (GATE) with few groups, when selection into treatment is not too strong. However, for finer causal heterogeneity, explicitly outcome-centred forest-based approaches are superior. The mcf has three additional benefits: (i) It is the most robust estimator in cases when dml-based approaches underperform because of substantial selection into treatment; (ii) it is the best estimator for GATEs when the number of groups gets larger; and (iii), it is the only estimator that is internally consistent, in the sense that low-dimensional causal ATEs and GATEs are obtained as aggregates of finer-grained causal parameters.
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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 | chernozhukov2018dml APACrefauthors Chernozhukov, V. , Chetverikov, D… (2018) 2018 | 1.000 | 10 | 3 | 100% |
| 2 | lechner2018modified APACrefauthors Lechner, M. APACrefauthors \ (1812) 2018 | 0.965 | 10 | 5 | 90% |
| 3 | robins1995semiparametric APACrefauthors Robins, J M. \ Rotnitzky, A.… (1995) 1995 | 0.950 | 7 | 3 | 86% |
| 4 | athey2019grf APACrefauthors Athey, S. , Tibshirani, J. \ Wager, S. A… (2019) 2019 | 0.947 | 20 | 6 | 85% |
| 5 | knaus2022double APACrefauthors Knaus, M C. APACrefauthors \ (2022) 2022 | 0.843 | 4 | 4 | 75% |
| 6 | semenova2021debiased APACrefauthors Semenova, V. \ Chernozhukov, V.… (2021) 2021 | 0.843 | 4 | 3 | 75% |
| 7 | bach2024dmlr APACrefauthors Bach, P. , Kurz, M S. , Chernozhukov, V.… (2024) 2024 | 0.843 | 3 | 3 | 100% |
| 8 | kennedy2023towards APACrefauthors Kennedy, E H. APACrefauthors \ (2023) 2023 | 0.811 | 4 | 2 | 100% |
| 9 | wager2018estimation APACrefauthors Wager, S. \ Athey, S. APACrefauth… (2018) 2018 | 0.767 | 31 | 5 | 45% |
| 10 | knaus2021machine APACrefauthors Knaus, M C. , Lechner, M. \ Strittma… (2021) 2021 self | 0.737 | 3 | 2 | 100% |
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