Cover Image

Discovering Heterogeneous Treatment Effects in Regression Discontinuity Designs

Fuente: arXiv

Saved in:

Bibliographic Details
Main Author:	Reguly, Ágoston
Format:	Preprint
Published:	2021
Subjects:	Econometrics Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

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

Modelling with Sensitive Variables
by: Chan, Felix, et al.
Published: (2024)

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

Heterogeneous Treatment Effects in Panel Data
by: Levi, Retsef, et al.
Published: (2024)

Regression Discontinuity Design with Distribution-Valued Outcomes
by: Van Dijcke, David
Published: (2025)

A Note on Doubly Robust Estimator in Regression Discontinuity Designs
by: Kato, Masahiro
Published: (2024)

On Extrapolation of Treatment Effects in Multiple-Cutoff Regression Discontinuity Designs
by: Okamoto, Yuta, et al.
Published: (2024)

Detecting and Mitigating Group Bias in Heterogeneous Treatment Effects
by: Persson, Joel, et al.
Published: (2026)

Heterogeneous Treatment Effect Bounds under Sample Selection with an Application to the Effects of Social Media on Political Polarization
by: Heiler, Phillip
Published: (2022)

Efficient Adaptive Experimental Design for Average Treatment Effect Estimation
by: Kato, Masahiro, et al.
Published: (2020)

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

Persuasion Effects in Regression Discontinuity Designs
by: Jun, Sung Jae, et al.
Published: (2025)

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

Regression Discontinuity Design with Spillovers
by: Auerbach, Eric, et al.
Published: (2024)

Linear Multidimensional Regression with Interactive Fixed-Effects
by: Freeman, Hugo
Published: (2022)

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

Testing Full Mediation of Treatment Effects and the Identifiability of Causal Mechanisms
by: Huber, Martin, et al.
Published: (2026)

Treatment Effects in the Regression Discontinuity Model with Counterfactual Cutoff and Distorted Running Variables
by: Liao, Moyu
Published: (2025)

Efficient and Scalable Estimation of Distributional Treatment Effects with Multi-Task Neural Networks
by: Hirata, Tomu, et al.
Published: (2025)

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

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

High-Dimensional Tail Index Regression
by: Sasaki, Yuya, et al.
Published: (2024)

On LASSO for High Dimensional Predictive Regression
by: Mei, Ziwei, et al.
Published: (2022)

Horseshoe Prior Bayesian Quantile Regression
by: Kohns, David, et al.
Published: (2020)

Using Forests in Multivariate Regression Discontinuity Designs
by: Liu, Yiqi, et al.
Published: (2023)

Global Testing in Multivariate Regression Discontinuity Designs
by: Samiahulin, Artem
Published: (2026)

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

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

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

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

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

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

Manipulation-Robust Regression Discontinuity Designs
by: Ishihara, Takuya, et al.
Published: (2020)

Leveraging Covariates in Regression Discontinuity Designs
by: Cattaneo, Matias D., et al.
Published: (2025)

Semiparametric Inference for Regression-Discontinuity Designs
by: Jiang, Weiwei, et al.
Published: (2024)

Inference for Regression with Variables Generated by AI or Machine Learning
by: Battaglia, Laura, et al.
Published: (2024)

Unemployment Dynamics Forecasting with Machine Learning Regression Models
by: Kim, Kyungsu
Published: (2025)

Nuclear Norm Regularized Estimation of Panel Regression Models
by: Moon, Hyungsik Roger, et al.
Published: (2018)

Gradient Boosting for Spatial Regression Models with Autoregressive Disturbances
by: Balzer, Michael
Published: (2025)

Improving the Finite Sample Estimation of Average Treatment Effects using Double/Debiased Machine Learning with Propensity Score Calibration
by: Ballinari, Daniele, et al.
Published: (2024)