Results 121 to 130 of about 1,239,696 (252)
Robust causal inference using directed acyclic graphs: the R package 'dagitty'.
International Journal of Epidemiology, 2017 J. Textor +4 more
semanticscholar +1 more source
SSRN Electronic Journal, 2017 Engineers often need to understand how to deploy new innovations to maximize impact in real-time environments. For collaborations to succeed, researchers must understand and communicate statistical causal inference in ways that are consistent with unstructured settings where estimates can change in real-time.
openaire +1 more source
An optimal transport approach to estimating causal effects via nonlinear difference-in-differences
Journal of Causal InferenceWe propose a nonlinear difference-in-differences (DiD) method to estimate multivariate counterfactual distributions in classical treatment and control study designs with observational data.
Torous William +2 more
doaj +1 more source
On the validity of covariate adjustment for estimating causal effects
, 2010 Identifying effects of actions (treatments) on outcome variables from observational data and causal assumptions is a fundamental problem in causal inference.
Robins, J. +2 more
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Bootstrap Inference for K-Nearest Neighbour Matching Estimators [PDF]
Abadie and Imbens (2008, Econometrica) showed that classical bootstrap schemes fail to provide correct inference for K-nearest neighbour (KNN) matching estimators of average causal effects.
de Luna, Xavier +2 more
core +2 more sources
SOME METHODS FOR CAUSAL INFERENCE, WITH APPLICATION TO AN OBSERVATIONAL EPILEPSY DATA SET
, 2021 N/ACausal inference attempts to attribute a causal mechanism to a treatment in an observational study. Attributing cause is a major focus of research in bio-statistics and application to observational biomedical studies.
Wang, Mengyao
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Causal Inference is Necessary but Insufficient for Causal Inference. [PDF]
International Journal of Epidemiology, 2015 openaire +1 more source
Causal Inference in Comparative and International Education
All phenomena in the social sciences are shaped by a complex web of variables, and using data to detect patterns and trends in such variables is important.
Kameshwara, K., Gorman, E.
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Marginal Structural Models and Causal Inference in Epidemiology
Epidemiology, 2000 J. Robins, M. Hernán, B. Brumback
semanticscholar +1 more source
Discovering cyclic causal models in psychological research [PDF]
Statistical network models have become popular tools for analyzing multivariate psychological data. In empirical practice, network parameters are often interpreted as reflecting causal relationships – an approach that can be characterized as a form of ...
Waldorp, Lourens J +2 more
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