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Nonparametric estimation of conditional incremental effects

Journal of Causal Inference
Conditional effect estimation has great scientific and policy importance because interventions may impact subjects differently depending on their characteristics.
McClean Alec, Branson Zach, Kennedy Edward H. +2 more
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Doubly weighted M-estimation for nonrandom assignment and missing outcomes

Journal of Causal Inference
This article proposes a class of M-estimators that double weight for the joint problems of nonrandom treatment assignment and missing outcomes. Identification of the main parameter of interest is achieved under unconfoundedness and missing at random ...
Negi Akanksha
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Evaluating Boolean relationships in Configurational Comparative Methods

Journal of Causal Inference
Configurational Comparative Methods (CCMs) aim to learn causal structures from datasets by exploiting Boolean sufficiency and necessity relationships. One important challenge for these methods is that such Boolean relationships are often not satisfied in
De Souter Luna
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Bridging binarization: causal inference with dichotomized continuous exposures. [PDF]

J Causal Inference
Lee K, Hubbard A, Schuler A.
europepmc +1 more source

A comparison of causal inference methods for evaluating multiple treatment groups. [PDF]

J Nonparametr Stat
Chen S, Wu H, Zhao H.
europepmc +1 more source

Bias of the additive hazard model in the presence of causal effect heterogeneity. [PDF]

Lifetime Data Anal
Post RAJ, van den Heuvel ER, Putter H.
europepmc +1 more source

Mediation Analysis using Semi-parametric Shape-Restricted Regression with Applications. [PDF]