arXiv 7 May 2026 · Econometrics
arXiv:2605.06386 · PDF · DOI · OpenAlex · Extracted main text
This position paper argues that, in debiased machine learning, balancing functions should be derived from the Neyman orthogonal score, not chosen only as functions of covariates. Covariate balancing is effective when the regression error entering the score can be represented by functions of covariates alone, and it is the natural finite-dimensional approximation for targets such as ATT counterfactual means. For ATE estimation under treatment effect heterogeneity, however, the score error generally contains treatment-specific components because the outcome regression is a function of the full regressor $X=(D,Z)$. In that case, balancing common functions of $Z$ can leave the treatment-specific component unbalanced. We therefore advocate regressor balancing, implemented by Riesz regression with basis functions of $X$, as the general balancing principle for DML. The position is not that covariate balancing is invalid, but that covariate balancing should be understood as the special case that is appropriate when the score-relevant regression error is a function of covariates alone.
appendix boundary found by appendix_command · 54% 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 | Jens Hainmueller (2012) Entropy balancing for causal effects: A multivariate reweighting method to produce balanced samples in observational studies | 1.000 | 6 | 3 | 100% |
| 2 | Masahiro Kato (2026) A unified framework for debiased machine learning: Riesz representer fitting under bregman divergence, 2026 self | 0.843 | 3 | 3 | 100% |
| 3 | José R. Zubizarreta (2015) Stable weights that balance covariates for estimation with incomplete outcome data | 0.811 | 4 | 2 | 100% |
| 4 | Victor Chernozhukov, Denis Chetverikov, Mert Demirer, Esther Duflo,… (2018) Double/debiased machine learning for treatment and structural parameters | 0.737 | 3 | 2 | 100% |
| 5 | Kosuke Imai and Marc Ratkovic (2013) Covariate balancing propensity score | 0.737 | 3 | 2 | 100% |
| 6 | Aad W. van der Vaart (1998) Asymptotic Statistics | 0.737 | 3 | 2 | 100% |
| 7 | David Bruns-Smith, Oliver Dukes, Avi Feller, and Elizabeth L Ogburn (2025) Augmented balancing weights as linear regression | 0.644 | 2 | 2 | 100% |
| 8 | Jianqing Fan, Kosuke Imai, Inbeom Lee, Han Liu, Yang Ning, and Xiaol… (2021) Optimal covariate balancing conditions in propensity score estimation, 2021 | 0.644 | 2 | 2 | 100% |
| 9 | Qingyuan Zhao (2019) Covariate balancing propensity score by tailored loss functions | 0.644 | 2 | 2 | 100% |
| 10 | Eli Ben-Michael, Avi Feller, David A. Hirshberg, and José R. Zubizar… (2021) The balancing act in causal inference, 2021 | 0.405 | 1 | 1 | 100% |
Showing the top 10 of 18 scored citations.