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Journal of Causal Inference

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JOURNAL INFORMATION
Publisher Walter de Gruyter GmbH
Open Access YES
Quartile Q2
Citation Count 112
P-ISSN 2193-3685
E-ISSN 2193-3677
Categories Statistics and Probability, Statistics, Probability and Uncertainty
Area Decision Sciences, Mathematics
Region Western Europe
Country Germany
Summary

  1. Journal of Causal Inference is published by Walter de Gruyter GmbH
  2. It is an oppen access journal
  3. The Subject Area : Decision Sciences; Mathematics

Latest Articles published

Last updated: 11/11/2024

The functional average treatment effect


Last updated: 11/11/2024

Double machine learning and design in batch adaptive experiments


Last updated: 04/11/2024

Prospective and retrospective causal inferences based on the potential outcome framework


Last updated: 04/11/2024

Causal inference with textual data: A quasi-experimental design assessing the association between author metadata and acceptance among ICLR submissions from 2017 to 2022


Last updated: 02/09/2024

From urn models to box models: Making Neyman's (1923) insights accessible


Last updated: 13/08/2024

Neyman meets causal machine learning: Experimental evaluation of individualized treatment rules


Last updated: 13/08/2024

Estimation of network treatment effects with non-ignorable missing confounders


Last updated: 13/08/2024

An optimal transport approach to estimating causal effects via nonlinear difference-in-differences


Last updated: 29/07/2024

Potential outcomes and decision-theoretic foundations for statistical causality: Response to Richardson and Robins


Last updated: 29/07/2024

Quantifying the quality of configurational causal models