Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome
Fuente:
arXiv
Salvato in:
| Autori principali: | , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866912919724228608 |
|---|---|
| author | Zuo, Shuozhi Wang, Yixin Yang, Fan |
| author_facet | Zuo, Shuozhi Wang, Yixin Yang, Fan |
| contents | Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational studies, covariates, treatment, and outcomes are often only partially observed. When missingness depends on unobserved values (missing not at random; MNAR), standard methods can yield biased estimates of the conditional average treatment effect (CATE). This paper establishes nonparametric identification of the CATE under multivariate MNAR mechanisms that allow covariates, treatment, and outcomes to be MNAR. It also develops nonparametric and parametric estimators and proposes a sensitivity analysis framework for assessing robustness to violations of the missingness assumptions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2602_19378 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome Zuo, Shuozhi Wang, Yixin Yang, Fan Methodology Treatment effect heterogeneity is central to policy evaluation, social science, and precision medicine, where interventions can affect individuals differently. In observational studies, covariates, treatment, and outcomes are often only partially observed. When missingness depends on unobserved values (missing not at random; MNAR), standard methods can yield biased estimates of the conditional average treatment effect (CATE). This paper establishes nonparametric identification of the CATE under multivariate MNAR mechanisms that allow covariates, treatment, and outcomes to be MNAR. It also develops nonparametric and parametric estimators and proposes a sensitivity analysis framework for assessing robustness to violations of the missingness assumptions. |
| title | Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome |
| topic | Methodology |
| url | https://arxiv.org/abs/2602.19378 |