Copertina

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Persson, Joel, Bakker, Jurriën, Bohle, Dennis, Feuerriegel, Stefan, von Wangenheim, Florian
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Methodology Machine Learning Econometrics
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Educational Effects in Mathematics: Conditional Average Treatment Effect depending on the Number of Treatments
di: Nagai, Tomoko, et al.
Pubblicazione: (2024)

Dynamic Local Average Treatment Effects
di: Sojitra, Ravi B., et al.
Pubblicazione: (2024)

Direct Bias-Correction Term Estimation for Average Treatment Effect Estimation
di: Kato, Masahiro
Pubblicazione: (2025)

Estimating Dyadic Treatment Effects with Unknown Confounders
di: Hoshino, Tadao, et al.
Pubblicazione: (2024)

An Algorithm for Identifying Interpretable Subgroups With Elevated Treatment Effects
di: Chiu, Albert
Pubblicazione: (2025)

Identification of Average Treatment Effects in Nonparametric Panel Models
di: Athey, Susan, et al.
Pubblicazione: (2025)

Robust and Agnostic Learning of Conditional Distributional Treatment Effects
di: Kallus, Nathan, et al.
Pubblicazione: (2022)

Double Robust Bayesian Inference on Average Treatment Effects
di: Breunig, Christoph, et al.
Pubblicazione: (2022)

Synthetic Blips: Generalizing Synthetic Controls for Dynamic Treatment Effects
di: Agarwal, Anish, et al.
Pubblicazione: (2022)

A Large-Scale Empirical Comparison of Meta-Learners and Causal Forests for Heterogeneous Treatment Effect Estimation in Marketing Uplift Modeling
di: Singh, Aman
Pubblicazione: (2026)

Bias-Reduced Estimation of Finite Mixtures: An Application to Latent Group Structures in Panel Data
di: Langevin, Raphaël
Pubblicazione: (2026)

Triple/Debiased Lasso for Statistical Inference of Conditional Average Treatment Effects
di: Kato, Masahiro
Pubblicazione: (2024)

Active Adaptive Experimental Design for Treatment Effect Estimation with Covariate Choices
di: Kato, Masahiro, et al.
Pubblicazione: (2024)

The Honest Truth About Causal Trees: Accuracy Limits for Heterogeneous Treatment Effect Estimation
di: Cattaneo, Matias D., et al.
Pubblicazione: (2025)

Estimating Treatment Effects using Multiple Surrogates: The Role of the Surrogate Score and the Surrogate Index
di: Athey, Susan, et al.
Pubblicazione: (2016)

Estimating Treatment Effects under Algorithmic Interference: A Structured Neural Networks Approach
di: Zhan, Ruohan, et al.
Pubblicazione: (2024)

Robust Inference Methods for Latent Group Panel Models under Possible Group Non-Separation
di: Akgun, Oguzhan, et al.
Pubblicazione: (2025)

Covariate-Adjusted Deep Causal Learning for Heterogeneous Panel Data Models
di: Zhou, Guanhao, et al.
Pubblicazione: (2025)

disco: Distributional Synthetic Controls
di: Gunsilius, Florian, et al.
Pubblicazione: (2025)

Shrinkage-Based Regressions with Many Related Treatments
di: Dilber, Enes, et al.
Pubblicazione: (2025)

Optimal Bias-Correction and Valid Inference in High-Dimensional Ridge Regression: A Closed-Form Solution
di: Gao, Zhaoxing, et al.
Pubblicazione: (2024)

Group Shapley Value and Counterfactual Simulations in a Structural Model
di: Kwon, Yongchan, et al.
Pubblicazione: (2024)

Improving the Estimation of Lifetime Effects in A/B Testing via Treatment Locality
di: Chen, Shuze, et al.
Pubblicazione: (2024)

Estimation of Optimal Dynamic Treatment Assignment Rules under Policy Constraints
di: Sakaguchi, Shosei
Pubblicazione: (2021)

Fast Online Changepoint Detection
di: Ghezzi, Fabrizio, et al.
Pubblicazione: (2024)

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

TEA-Time: Transporting Effects Across Time
di: Parikh, Harsh, et al.
Pubblicazione: (2026)

Linear Multidimensional Regression with Interactive Fixed-Effects
di: Freeman, Hugo
Pubblicazione: (2022)

Data Fusion for Partial Identification of Causal Effects
di: Lanners, Quinn, et al.
Pubblicazione: (2025)

Double Machine Learning of Continuous Treatment Effects with General Instrumental Variables
di: Chen, Shuyuan, et al.
Pubblicazione: (2026)

Structure-agnostic Optimality of Doubly Robust Learning for Treatment Effect Estimation
di: Jin, Jikai, et al.
Pubblicazione: (2024)

What Makes Treatment Effects Identifiable? Characterizations and Estimators Beyond Unconfoundedness
di: Cai, Yang, et al.
Pubblicazione: (2025)

Estimating Causal Effects with Double Machine Learning -- A Method Evaluation
di: Fuhr, Jonathan, et al.
Pubblicazione: (2024)

Treatment Effect Heterogeneity in Regression Discontinuity Designs
di: Calonico, Sebastian, et al.
Pubblicazione: (2025)

Estimating Treatment Effects Under Bounded Heterogeneity
di: Kwon, Soonwoo, et al.
Pubblicazione: (2025)

Modeling Covariate Transition for Efficient Estimation of Longitudinal Treatment Effects in Randomized Experiments
di: Chihara, Naoki, et al.
Pubblicazione: (2026)

Bayesian Semiparametric Causal Inference: Targeted Doubly Robust Estimation of Treatment Effects
di: Sert, Gözde, et al.
Pubblicazione: (2025)

xtdml: Double Machine Learning Estimation to Static Panel Data Models with Fixed Effects in R
di: Polselli, Annalivia
Pubblicazione: (2025)

Effect Identification and Unit Categorization in the Multi-Score Regression Discontinuity Design with Application to LED Manufacturing
di: Schwarz, Philipp Alexander, et al.
Pubblicazione: (2025)

Semi-Supervised Treatment Effect Estimation with Unlabeled Covariates for Prediction-Powered Causal Inference
di: Kato, Masahiro
Pubblicazione: (2025)