Results 61 to 70 of about 1,239,696 (252)

Causal inference with observational data: the need for triangulation of evidence

Psychological Medicine, 2021
The goal of much observational research is to identify risk factors that have a causal effect on health and social outcomes. However, observational data are subject to biases from confounding, selection and measurement, which can result in an ...
G. Hammerton, M. Munafo
semanticscholar   +1 more source

Principal Stratification in Causal Inference [PDF]

Biometrics, 2002
Summary. Many scientific problems require that treatment comparisons be adjusted for posttreatment variables, but the estimands underlying standard methods are not causal effects. To address this deficiency, we propose a general framework for comparing treatments adjusting for posttreatment variables that yields principal effects based on principal ...
Frangakis, Constantine E., Rubin, Donald B.   +1 more
openaire   +2 more sources

Causal Inference with Deep Causal Graphs

CoRR, 2020
Supplementary material can be found in https://github.com/aparafita/dcg ...
Álvaro Parafita, Jordi Vitrià
openaire   +2 more sources

Implementing Causal Inference in Ecology Through the Structural Causal Model (SCM) Framework

, 2022
Ecologists are often interested in understanding causal relationships from ecological data. However, developed methods for causal inference, particularly for observational-based studies are often not taught or applied in ecology.
Arif, Suchinta
core  

Interventional Approach for Path-Specific Effects

Journal of Causal Inference, 2017
Standard causal mediation analysis decomposes the total effect into a direct effect and an indirect effect in settings with only one single mediator.
Lin Sheng-Hsuan, VanderWeele Tyler
doaj   +1 more source

A Kernel-Based Metric for Balance Assessment

Journal of Causal Inference, 2018
An important goal in causal inference is to achieve balance in the covariates among the treatment groups. In this article, we introduce the concept of distributional balance preserving which requires the distribution of the covariates to be the same in ...
Zhu Yeying, Savage Jennifer S., Ghosh Debashis   +2 more
doaj   +1 more source

The variance of causal effect estimators for binary v-structures

Journal of Causal Inference, 2022
Adjusting for covariates is a well-established method to estimate the total causal effect of an exposure variable on an outcome of interest. Depending on the causal structure of the mechanism under study, there may be different adjustment sets, equally ...
Kuipers Jack, Moffa Giusi
doaj   +1 more source

Causal Inference in DotA 2 when estimated through randomized data

, 2022
Strategy games could be considered as an amazing playground for using Causal inference methods. The complex nature of the data and the built-in randomization help with testing causal inference in a scenario where in reality it would be hard and expensive.
Avgousti, Stelios (author)
core  

Learning Heterogeneity in Causal Inference Using Sufficient Dimension Reduction

Journal of Causal Inference, 2019
Often the research interest in causal inference is on the regression causal effect, which is the mean difference in the potential outcomes conditional on the covariates. In this paper, we use sufficient dimension reduction to estimate a lower dimensional
Luo Wei, Wu Wenbo, Zhu Yeying
doaj   +1 more source

Identifying HIV sequences that escape antibody neutralization using random forests and collaborative targeted learning

Journal of Causal Inference, 2022
Recent studies have indicated that it is possible to protect individuals from HIV infection using passive infusion of monoclonal antibodies. However, in order for monoclonal antibodies to confer robust protection, the antibodies must be capable of ...
Jin Yutong, Benkeser David
doaj   +1 more source