Cover Image

Debiasing and $t$-tests for synthetic control inference on average causal effects

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Chernozhukov, Victor, Wuthrich, Kaspar, Zhu, Yinchu
Format:	Preprint
Published:	2018
Subjects:	Econometrics
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Estimating Causal Effects of Discrete and Continuous Treatments with Binary Instruments
by: Chernozhukov, Victor, et al.
Published: (2024)

Minimax Semiparametric Learning With Approximate Sparsity
by: Bradic, Jelena, et al.
Published: (2019)

Pairwise Valid Instruments
by: Sun, Zhenting, et al.
Published: (2022)

New possibilities in identification of binary choice models with fixed effects
by: Zhu, Yinchu
Published: (2022)

A model of multiple hypothesis testing
by: Viviano, Davide, et al.
Published: (2021)

The Power of Tests for Detecting $p$-Hacking
by: Elliott, Graham, et al.
Published: (2022)

IV regression with distribution-valued outcomes
by: Van Dijcke, David, et al.
Published: (2026)

An Introduction to Double/Debiased Machine Learning
by: Ahrens, Achim, et al.
Published: (2025)

Selection and parallel trends
by: Ghanem, Dalia, et al.
Published: (2022)

Bias correction for quantile regression estimators
by: Franguridi, Grigory, et al.
Published: (2020)

Quantile optimization in semidiscrete optimal transport
by: Zhu, Yinchu, et al.
Published: (2026)

Semidiscrete optimal transport with unknown costs
by: Zhu, Yinchu, et al.
Published: (2023)

Double/Debiased Machine Learning for Treatment and Causal Parameters
by: Chernozhukov, Victor, et al.
Published: (2016)

Uniform Inference on High-dimensional Spatial Panel Networks
by: Chernozhukov, Victor, et al.
Published: (2021)

Comment on "Sequential validation of treatment heterogeneity" and "Comment on generic machine learning inference on heterogeneous treatment effects in randomized experiments"
by: Chernozhukov, Victor, et al.
Published: (2025)

Welfare Analysis in Dynamic Models
by: Chernozhukov, Victor, et al.
Published: (2019)

Arellano-Bond LASSO Estimator for Dynamic Linear Panel Models
by: Chernozhukov, Victor, et al.
Published: (2024)

Plausible GMM: A Quasi-Bayesian Approach
by: Chernozhukov, Victor, et al.
Published: (2025)

Individual and group fairness in geographical partitioning
by: Ryzhov, Ilya O., et al.
Published: (2025)

Policy Learning with Confidence
by: Chernozhukov, Victor, et al.
Published: (2025)

Credible causal inference beyond toy models
by: Bastías, Pablo Geraldo
Published: (2024)

Dynamic causal inference with time series data
by: Schaffe-Odeleye, Tanique, et al.
Published: (2026)

Adversarial Estimation of Riesz Representers
by: Chernozhukov, Victor, et al.
Published: (2020)

Conditional Influence Functions
by: Chernozhukov, Victor, et al.
Published: (2024)

Double machine learning for causal inference in a multivariate sample selection model
by: Dolgikh, Sofiia, et al.
Published: (2025)

Automatic Debiased Machine Learning via Riesz Regression
by: Chernozhukov, Victor, et al.
Published: (2021)

Sensitivity Analysis for Causal ML: A Use Case at Booking.com
by: Bach, Philipp, et al.
Published: (2025)

Hyperparameter Tuning for Causal Inference with Double Machine Learning: A Simulation Study
by: Bach, Philipp, et al.
Published: (2024)

Simple robust two-stage estimation and inference for generalized impulse responses and multi-horizon causality
by: Dufour, Jean-Marie, et al.
Published: (2024)

Agentic Economic Modeling
by: Zhang, Bohan, et al.
Published: (2025)

On the relationship between prediction intervals, tests of sharp nulls and inference on realized treatment effects in settings with few treated units
by: Alvarez, Luis, et al.
Published: (2025)

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

Identifying causal effects with subjective ordinal outcomes
by: Goff, Leonard
Published: (2022)

Survey calibration for causal inference: a simple method to balance covariate distributions
by: Beręsewicz, Maciej
Published: (2023)

Local Gaussian copula inference with structural breaks: testing dependence predictability
by: Mayer, Alexander, et al.
Published: (2026)

Cluster-robust inference with a single treated cluster using the t-test
by: Lau, Chun Pong, et al.
Published: (2025)

Applied Causal Inference Powered by ML and AI
by: Chernozhukov, Victor, et al.
Published: (2024)

Debiased Machine Learning U-statistics
by: Escanciano, Juan Carlos, et al.
Published: (2022)

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

Formalising causal inference as prediction on a target population
by: Höltgen, Benedikt, et al.
Published: (2024)