Results 61 to 70 of about 117 (76)

Highly adaptive Lasso for estimation of heterogeneous treatment effects and treatment recommendation

Journal of Causal Inference
The estimation of conditional average treatment effects (CATEs) is an important problem in many applications. Many machine learning-based frameworks for such estimation have been proposed, including meta-learning, causal trees, and causal forests ...
Nizam Sohail, Codi Allison, Rogawski McQuade Elizabeth, Benkeser David   +3 more
doaj   +1 more source

A Geometric Perspective on Double Robustness by Semiparametric Theory and Information Geometry

Double robustness (DR) is a widely-used property of estimators that provides protection against model misspecification and slow convergence of nuisance functions.
Ying, Andrew
core  

Decision making, symmetry and structure: Justifying causal interventions

Journal of Causal Inference
We can use structural causal models (SCMs) to help us evaluate the consequences of actions given data. SCMs identify actions with structural interventions. A careful decision maker may wonder whether this identification is justified.
Johnston David O., Ong Cheng Soon, Williamson Robert C.   +2 more
doaj   +1 more source

Dual Likelihood for Causal Inference under Structure Uncertainty

Knowledge of the underlying causal relations is essential for inferring the effect of interventions in complex systems. In a widely studied approach, structural causal models postulate noisy functional relations among interacting variables, where the ...
Drton, Mathias, Strieder, David
core  

Direct, indirect, and interaction effects based on principal stratification with a binary mediator

Journal of Causal Inference
Given a binary treatment and a binary mediator, mediation analysis decomposes the total effect of the treatment on an outcome variable into various sub-effects, and there appeared two-, three-, and four-way decompositions in the literature.
Lee Myoung-jae
doaj   +1 more source

Ancestor regression in structural vector autoregressive models

Journal of Causal Inference
We present a new method for causal discovery in linear structural vector autoregressive models. We adapt an idea designed for independent observations to the case of time series while retaining its favorable properties, i.e., explicit error control for ...
Schultheiss Christoph, Ulmer Markus, Bühlmann Peter   +2 more
doaj   +1 more source

Recovery and inference of causal effects with sequential adjustment for confounding and attrition

Journal of Causal Inference
Confounding bias and selection bias bring two significant challenges to the validity of conclusions drawn from applied causal inference. The latter can stem from informative missingness, such as in cases of attrition.
de Aguas Johan, Pensar Johan, Varnet Pérez Tomás, Biele Guido   +3 more
doaj   +1 more source

Geodesic Causal Inference

Adjusting for confounding and imbalance when establishing statistical relationships is an increasingly important task, and causal inference methods have emerged as the most popular tool to achieve this.
Kurisu, Daisuke, Müller, Hans-Georg, Otsu, Taisuke, Zhou, Yidong   +3 more
core  

Identifying Total Causal Effects in Linear Models under Partial Homoscedasticity

A fundamental challenge of scientific research is inferring causal relations based on observed data. One commonly used approach involves utilizing structural causal models that postulate noisy functional relations among interacting variables.
Drton, Mathias, Strieder, David
core  

Goddard range and range rate system Design evaluation report [PDF]

Tracking and telemetry data at VHF and S band frequencies from spacecraft for GRARR ...

core   +1 more source