Cover Image

Efficient estimation of average treatment effects with unmeasured confounding and proxies

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Ai, Chunrong, Shan, Jiawei
Format:	Preprint
Published:	2025
Subjects:	Methodology Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Kernel estimation of average treatment effects in models with unmeasured confounders
by: Chunrong Ai, et al.
Published: (2025)

Data-Automated Policy Learning for Nonlinear Welfare
by: Ai, Chunrong, et al.
Published: (2026)

A sensitivity analysis for the average derivative effect
by: Zhang, Jeffrey
Published: (2025)

On the estimation of leverage effect and volatility of volatility in the presence of jumps
by: Liu, Qiang, et al.
Published: (2025)

A joint modeling approach to treatment effects estimation with unmeasured confounders
by: Lee, Namhwa, et al.
Published: (2024)

Extracting Mechanisms from Heterogeneous Effects: An Identification Strategy for Mediation Analysis
by: Fu, Jiawei
Published: (2024)

Comment on "Generic machine learning inference on heterogeneous treatment effects in randomized experiments."
by: Imai, Kosuke, et al.
Published: (2025)

Heterogeneous Treatment Effects and Causal Mechanisms
by: Fu, Jiawei, et al.
Published: (2024)

Reinterpreting demand estimation
by: Chen, Jiafeng
Published: (2025)

An average-case sensitivity analysis for unmeasured confounding
by: Zhang, Yao, et al.
Published: (2022)

Testing identification in mediation and dynamic treatment models
by: Huber, Martin, et al.
Published: (2024)

The purpose of an estimator is what it does: Misspecification, estimands, and over-identification
by: Andrews, Isaiah, et al.
Published: (2025)

Partial identification via conditional linear programs: estimation and policy learning
by: Ben-Michael, Eli
Published: (2025)

High-dimensional estimation of quadratic variation based on penalized realized variance
by: Christensen, Kim, et al.
Published: (2021)

A simple distributional difference-in-differences estimator for univariate and bivariate outcomes
by: Fernández-Val, Iván, et al.
Published: (2024)

Functional instrumental variable regression with an application to estimating the impact of immigration on native wages
by: Seong, Dakyung, et al.
Published: (2021)

Dynamic covariate balancing: estimating treatment effects over time with potential local projections
by: Viviano, Davide, et al.
Published: (2021)

Causal Inference for Experiments with Latent Outcomes: Key Results and Their Implications for Design and Analysis
by: Fu, Jiawei, et al.
Published: (2025)

Nonparametric Identification and Estimation of Causal Effects on Latent Outcomes
by: Fu, Jiawei, et al.
Published: (2026)

Asymptotic equivalence of Principal Components and Quasi Maximum Likelihood estimators in Large Approximate Factor Models
by: Barigozzi, Matteo
Published: (2023)

Estimating Effects of Long-Term Treatments
by: Huang, Shan, et al.
Published: (2023)

Treatment effects at the margin: Everyone is marginal
by: Deng, Haotian
Published: (2025)

Exposure effects are not automatically useful for policymaking
by: Auerbach, Eric, et al.
Published: (2024)

Efficient GMM and Weighting Matrix under Misspecification
by: Kang, Byunghoon
Published: (2026)

On a Debiased and Semiparametric Efficient Changes-in-Changes Estimator
by: Sun, Jinghao, et al.
Published: (2025)

Efficient Bias Correction for Cross-section and Panel Data
by: Hahn, Jinyong, et al.
Published: (2022)

Estimating causal effects of continuous-time dynamic treatments with unmeasured confounders
by: Zhu, Haiyan, et al.
Published: (2026)

Estimating peer effects in noisy, low-rank networks via network smoothing
by: Hayes, Alex, et al.
Published: (2026)

A projection based approach for interactive fixed effects panel data models
by: Keilbar, Georg, et al.
Published: (2022)

When are time series predictions causal? The potential system and dynamic causal effects
by: Carlson, Jacob, et al.
Published: (2026)

Doubly robust estimation of causal effects for random object outcomes with continuous treatments
by: Bhattacharjee, Satarupa, et al.
Published: (2025)

Linear estimation of global average treatment effects
by: Faridani, Stefan, et al.
Published: (2022)

Treatment effect extrapolation in the presence of unmeasured confounding
by: Riley, Stephanie, et al.
Published: (2025)

A New and Efficient Debiased Estimation of General Treatment Models by Balanced Neural Networks Weighting
by: Wu, Zeqi, et al.
Published: (2025)

Computationally Efficient Estimation of Large Probit Models
by: Ding, Patrick, et al.
Published: (2024)

On (in)consistency of M-estimators under contamination
by: Klooster, Jens, et al.
Published: (2025)

Partial identification and unmeasured confounding with multiple treatments and multiple outcomes
by: Kang, Suyeon, et al.
Published: (2023)

Seemingly unrelated Bayesian additive regression trees for cost-effectiveness analyses in healthcare
by: Esser, Jonas, et al.
Published: (2024)

Optimal estimation for regression discontinuity design with binary outcomes
by: Ishihara, Takuya, et al.
Published: (2025)

Identification and estimation for matrix time series CP-factor models
by: Chang, Jinyuan, et al.
Published: (2024)