Philipp Bach, Oliver Schacht, Victor Chernozhukov, Sven Klaassen, Martin Spindler
arXiv 7 Feb 2024 · Econometrics · 6 citations (OpenAlex)
arXiv:2402.04674 · PDF · DOI · OpenAlex · Extracted main text
Proper hyperparameter tuning is essential for achieving optimal performance of modern machine learning (ML) methods in predictive tasks. While there is an extensive literature on tuning ML learners for prediction, there is only little guidance available on tuning ML learners for causal machine learning and how to select among different ML learners. In this paper, we empirically assess the relationship between the predictive performance of ML methods and the resulting causal estimation based on the Double Machine Learning (DML) approach by Chernozhukov et al. (2018). DML relies on estimating so-called nuisance parameters by treating them as supervised learning problems and using them as plug-in estimates to solve for the (causal) parameter. We conduct an extensive simulation study using data from the 2019 Atlantic Causal Inference Conference Data Challenge. We provide empirical insights on the role of hyperparameter tuning and other practical decisions for causal estimation with DML. First, we assess the importance of data splitting schemes for tuning ML learners within Double Machine Learning. Second, we investigate how the choice of ML methods and hyperparameters, including recent AutoML frameworks, impacts the estimation performance for a causal parameter of interest. Third, we assess to what extent the choice of a particular causal model, as characterized by incorporated parametric assumptions, can be based on predictive performance metrics.
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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 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters self | 0.874 | 6 | 4 | 67% |
| 2 | Alexandre Belloni, Victor Chernozhukov, and Christian Hansen (2013) Inference on Treatment Effects after Selection among High-Dimensional Controls† self | 0.843 | 3 | 3 | 100% |
| 3 | Victor Chernozhukov, Whitney K. Newey, Victor Quintas-Martinez, and… (2022) Riesznet and forestriesz: Automatic debiased machine learning with neural nets and random forests, 2022 self | 0.737 | 3 | 3 | 67% |
| 4 | Michael C Knaus (2022) Double machine learning-based programme evaluation under unconfoundedness | 0.644 | 2 | 2 | 100% |
| 5 | Chi Wang and Qingyun Wu (1911) FLO: fast and lightweight hyperparameter optimization for automl | 0.644 | 2 | 2 | 100% |
| 6 | Tianqi Chen and Carlos Guestrin (2016) XGBoost: A scalable tree boosting system | 0.644 | 2 | 2 | 100% |
| 7 | F. Pedregosa, G. Varoquaux, A. Gramfort, V. Michel, B. Thirion, O. G… (2011) Scikit-learn: Machine learning in Python | 0.511 | 2 | 1 | 100% |
| 8 | Claudia Shi, David M. Blei, and Victor Veitch (2019) Adapting neural networks for the estimation of treatment effects, 2019 | 0.511 | 2 | 1 | 100% |
| 9 | James M Robins, Andrea Rotnitzky, and Lue Ping Zhao (1994) Estimation of regression coefficients when some regressors are not always observed | 0.405 | 1 | 1 | 100% |
| 10 | Ezequiel Smucler, Andrea Rotnitzky, and James M Robins (2019) A unifying approach for doubly-robust $_1$ regularized estimation of causal contrasts | 0.405 | 1 | 1 | 100% |
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