Post-treatment problems: What can we say about the effect of a treatment among sub-groups who (would) respond in some way?
Fuente:
arXiv
Saved in:
| Main Authors: | Hazlett, Chad, McMurry, Nina, Shinkre, Tanvi |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Demystifying and avoiding the OLS "weighting problem": Unmodeled heterogeneity and straightforward solutions
by: Shinkre, Tanvi, et al.
Published: (2024)
by: Shinkre, Tanvi, et al.
Published: (2024)
Causal progress with imperfect placebo treatments and outcomes
by: Rohde, Adam, et al.
Published: (2023)
by: Rohde, Adam, et al.
Published: (2023)
Sensitivity of weighted least squares estimators to omitted variables
by: Wainstein, Leonard, et al.
Published: (2025)
by: Wainstein, Leonard, et al.
Published: (2025)
Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
by: Hartman, Erin, et al.
Published: (2025)
by: Hartman, Erin, et al.
Published: (2025)
Kpop: A kernel balancing approach for reducing specification assumptions in survey weighting
by: Hartman, Erin, et al.
Published: (2021)
by: Hartman, Erin, et al.
Published: (2021)
Inference at the data's edge: Gaussian processes for modeling and inference under model-dependency, poor overlap, and extrapolation
by: Cho, Soonhong, et al.
Published: (2024)
by: Cho, Soonhong, et al.
Published: (2024)
Can we detect treatment effect waning from time-to-event data?
by: Musta, Eni, et al.
Published: (2025)
by: Musta, Eni, et al.
Published: (2025)
Química org nica / John McMurry ; trad. María Aurora Lanto Arriola, Jorge Hern ndez Lanto
by: McMurry, John
Published: (2008)
by: McMurry, John
Published: (2008)
Estimation and inference of average treatment effects under heterogeneous additive treatment effect model
by: Lu, Xin, et al.
Published: (2024)
by: Lu, Xin, et al.
Published: (2024)
Estimating causal effects of functional treatments with modified functional treatment policies
by: Jiang, Ziren, et al.
Published: (2026)
by: Jiang, Ziren, et al.
Published: (2026)
Two-stage least squares with treatment-covariate interactions for treatment effect heterogeneity
by: Zhao, Anqi, et al.
Published: (2025)
by: Zhao, Anqi, et al.
Published: (2025)
Identification and estimation of the conditional average treatment effect with nonignorable missing covariates, treatment, and outcome
by: Zuo, Shuozhi, et al.
Published: (2026)
by: Zuo, Shuozhi, et al.
Published: (2026)
Variable importance measures for heterogeneous treatment effects
by: Hines, Oliver J., et al.
Published: (2022)
by: Hines, Oliver J., et al.
Published: (2022)
Improving efficiency in transporting average treatment effects
by: Rudolph, Kara E., et al.
Published: (2023)
by: Rudolph, Kara E., et al.
Published: (2023)
Encoding and inference on separable effects for sustained treatments
by: Gonzalez-Perez, Ignacio, et al.
Published: (2025)
by: Gonzalez-Perez, Ignacio, et al.
Published: (2025)
Trustworthy assessment of heterogeneous treatment effect estimator
by: Gao, Zijun
Published: (2024)
by: Gao, Zijun
Published: (2024)
Efficient collaborative learning of the average treatment effect
by: Li, Sijia, et al.
Published: (2024)
by: Li, Sijia, et al.
Published: (2024)
Estimation of time-varying treatment effects using marginal structural models dependent on partial treatment history
by: Seya, Nodoka, et al.
Published: (2024)
by: Seya, Nodoka, et al.
Published: (2024)
Rerandomization for quantile treatment effects
by: Han, Tingxuan, et al.
Published: (2026)
by: Han, Tingxuan, et al.
Published: (2026)
Estimating average treatment effects when treatment data are absent in a target study
by: Wen, Lan, et al.
Published: (2025)
by: Wen, Lan, et al.
Published: (2025)
Average treatment effect on the treated, under lack of positivity
by: Liu, Yi, et al.
Published: (2023)
by: Liu, Yi, et al.
Published: (2023)
Augmented match weighted estimators for average treatment effects
by: Xu, Tanchumin, et al.
Published: (2023)
by: Xu, Tanchumin, et al.
Published: (2023)
Non-parametric assessment of the calibration of individualized treatment effects
by: Sadatsafavi, Mohsen, et al.
Published: (2025)
by: Sadatsafavi, Mohsen, et al.
Published: (2025)
Longitudinal weighted and trimmed treatment effects with flip interventions
by: McClean, Alec, et al.
Published: (2025)
by: McClean, Alec, et al.
Published: (2025)
Transporting treatment effects from difference-in-differences studies
by: Renson, Audrey, et al.
Published: (2023)
by: Renson, Audrey, et al.
Published: (2023)
On the asymptotic distributions of some test statistics for two-way contingency tables
by: Zhang, Qingyang
Published: (2024)
by: Zhang, Qingyang
Published: (2024)
Transporting treatment effects by calibrating large-scale observational outcomes
by: Li, Harrison H
Published: (2026)
by: Li, Harrison H
Published: (2026)
The causal effects of modified treatment policies under network interference
by: Balkus, Salvador V., et al.
Published: (2024)
by: Balkus, Salvador V., et al.
Published: (2024)
Doubly robust average treatment effect estimation for survival data
by: Lee, Byeonghee, et al.
Published: (2025)
by: Lee, Byeonghee, et al.
Published: (2025)
Identification and estimation of mediational effects of longitudinal modified treatment policies
by: Gilbert, Brian, et al.
Published: (2024)
by: Gilbert, Brian, et al.
Published: (2024)
Variable importance measures for heterogeneous treatment effects with survival outcome
by: Ziersen, Simon Christoffer, et al.
Published: (2024)
by: Ziersen, Simon Christoffer, et al.
Published: (2024)
Identifying sparse treatment effects in high-dimensional outcome spaces
by: Jeong, Yujin, et al.
Published: (2024)
by: Jeong, Yujin, et al.
Published: (2024)
Differential recall bias in estimating treatment effects in observational studies
by: Bong, Suhwan, et al.
Published: (2023)
by: Bong, Suhwan, et al.
Published: (2023)
Data fusion methods for the heterogeneity of treatment effect and confounding function
by: Yang, Shu, et al.
Published: (2020)
by: Yang, Shu, et al.
Published: (2020)
Modified treatment policy effect estimation with weighted energy distance
by: Jiang, Ziren, et al.
Published: (2023)
by: Jiang, Ziren, et al.
Published: (2023)
Modified treatment policies that depend on the natural history of treatment
by: Díaz, Iván, et al.
Published: (2026)
by: Díaz, Iván, et al.
Published: (2026)
Grace periods in comparative effectiveness studies of sustained treatments
by: Wanis, Kerollos Nashat, et al.
Published: (2022)
by: Wanis, Kerollos Nashat, et al.
Published: (2022)
Efficient semiparametric estimation of marginal treatment effects with genetic instrumental variables
by: Patel, Ashish, et al.
Published: (2026)
by: Patel, Ashish, et al.
Published: (2026)
Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
by: Zhu, Haiyan, et al.
Published: (2026)
by: Zhu, Haiyan, et al.
Published: (2026)
A joint modeling approach to treatment effects estimation with unmeasured confounders
by: Lee, Namhwa, et al.
Published: (2024)
by: Lee, Namhwa, et al.
Published: (2024)
Similar Items
-
Demystifying and avoiding the OLS "weighting problem": Unmodeled heterogeneity and straightforward solutions
by: Shinkre, Tanvi, et al.
Published: (2024) -
Causal progress with imperfect placebo treatments and outcomes
by: Rohde, Adam, et al.
Published: (2023) -
Sensitivity of weighted least squares estimators to omitted variables
by: Wainstein, Leonard, et al.
Published: (2025) -
Inference with weights: Residualization produces short, valid intervals for varying estimands and varying resampling processes
by: Hartman, Erin, et al.
Published: (2025) -
Kpop: A kernel balancing approach for reducing specification assumptions in survey weighting
by: Hartman, Erin, et al.
Published: (2021)