Results 41 to 50 of about 157 (97)

Single proxy synthetic control

Journal of Causal Inference
Synthetic control methods are widely used to estimate the treatment effect on a single treated unit in time-series settings. A common approach to estimate synthetic control weights is to regress the treated unit’s pretreatment outcome and covariates ...
Park Chan, Tchetgen Tchetgen Eric J.
doaj   +1 more source

Efficient and flexible mediation analysis with time-varying mediators, treatments, and confounders

Journal of Causal Inference, 2023
Understanding the mechanisms of action of interventions is a major general goal of scientific inquiry. The collection of statistical methods that use data to achieve this goal is referred to as mediation analysis.
Díaz Iván, Williams Nicholas, Rudolph Kara E.   +2 more
doaj   +1 more source

Matching estimators of causal effects in clustered observational studies

Journal of Causal Inference
Marine conservation preserves fish biodiversity, protects marine and coastal ecosystems, and supports climate resilience and adaptation. Despite the importance of establishing marine protected areas (MPAs), research on the effectiveness of MPAs with ...
Cui Can, Zhang Yunshu, Yang Shu, Reich Brian J., Gill David A.   +4 more
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Spillover detection for donor selection in synthetic control models

Journal of Causal Inference
Synthetic control (SC) models are widely used to estimate causal effects in settings with observational time-series data. To identify the causal effect on a target unit, SC requires the existence of additional units that are not impacted by the ...
O’Riordan Michael, Gilligan-Lee Ciarán M.   +1 more
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Causality of Functional Longitudinal Data

, 2023
"Treatment-confounder feedback" is the central complication to resolve in longitudinal studies, to infer causality. The existing frameworks for identifying causal effects for longitudinal studies with discrete repeated measures hinge heavily on assuming ...
Ying, Andrew
core  

Rate doubly robust estimation for weighted average treatment effects

Journal of Causal Inference
The weighted average treatment effect (WATE) defines a versatile class of causal estimands for populations characterized by propensity score weights, including the average treatment effect (ATE), treatment effect on the treated (ATT), on controls (ATC ...
Wang Yiming, Liu Yi, Yang Shu
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Representation of Context-Specific Causal Models with Observational and Interventional Data

, 2022
We consider the problem of representing causal models that encode context-specific information for discrete data using a proper subclass of staged tree models which we call CStrees. We show that the context-specific information encoded by a CStree can be
Duarte, Eliana, Solus, Liam
core  

Adding covariates to bounds: what is the question?

Journal of Causal Inference
Symbolic nonparametric bounds for partial identification of causal effects now have a long history in the causal literature. Sharp bounds, bounds that use all available information to make the range of values as narrow as possible, are often the goal ...
Jonzon Gustav, Gabriel Erin E., Sjölander Arvid, Sachs Michael C.   +3 more
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Greedy Causal Discovery is Geometric

, 2021
Finding a directed acyclic graph (DAG) that best encodes the conditional independence statements observable from data is a central question within causality.
Linusson, Svante, Restadh, Petter, Solus, Liam   +2 more
core  

All models are wrong, but which are useful? Comparing parametric and nonparametric estimation of causal effects in finite samples

Journal of Causal Inference, 2023
There is a long-standing debate in the statistical, epidemiological, and econometric fields as to whether nonparametric estimation that uses machine learning in model fitting confers any meaningful advantage over simpler, parametric approaches in finite ...
Rudolph Kara E., Williams Nicholas T., Miles Caleb H., Antonelli Joseph, Diaz Ivan   +4 more
doaj   +1 more source