Estimation of heterogeneous principal effects under principal ignorability
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arXiv
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| Format: | Preprint |
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2026
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| _version_ | 1866918380699648000 |
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| author | Zhang, Rui Doss, Charles R. Huling, Jared D. |
| author_facet | Zhang, Rui Doss, Charles R. Huling, Jared D. |
| contents | We study estimation and inference for heterogeneous principal causal effects with binary treatments and binary intermediate variables. Principal causal effects are subgroup effects within strata defined by potential values of an intermediate variable, including effects among compliers. We propose a framework for estimating and forming pointwise confidence intervals for heterogeneous principal causal effects under the principal ignorability assumption. Several estimators are developed, and their robustness properties are characterized: one estimator is doubly robust, whereas the other two attain intermediate robustness between double and triple robustness; in contrast, principal causal effects can be estimated in a triply robust manner only. We establish large-sample theory under nonparametric smoothness conditions and analyze the bias contributions of each approach, providing insight into performance beyond the smooth setting, including in high-dimensional regimes. Camden Coalition hotspotting randomized trial are used to illustrate the methods by estimating heterogeneous complier effects. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2603_08963 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Estimation of heterogeneous principal effects under principal ignorability Zhang, Rui Doss, Charles R. Huling, Jared D. Methodology Machine Learning We study estimation and inference for heterogeneous principal causal effects with binary treatments and binary intermediate variables. Principal causal effects are subgroup effects within strata defined by potential values of an intermediate variable, including effects among compliers. We propose a framework for estimating and forming pointwise confidence intervals for heterogeneous principal causal effects under the principal ignorability assumption. Several estimators are developed, and their robustness properties are characterized: one estimator is doubly robust, whereas the other two attain intermediate robustness between double and triple robustness; in contrast, principal causal effects can be estimated in a triply robust manner only. We establish large-sample theory under nonparametric smoothness conditions and analyze the bias contributions of each approach, providing insight into performance beyond the smooth setting, including in high-dimensional regimes. Camden Coalition hotspotting randomized trial are used to illustrate the methods by estimating heterogeneous complier effects. |
| title | Estimation of heterogeneous principal effects under principal ignorability |
| topic | Methodology Machine Learning |
| url | https://arxiv.org/abs/2603.08963 |