Results 31 to 40 of about 127 (80)

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

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
doaj   +1 more source

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
doaj   +1 more source

Do LLMs act as repositories of causal knowledge?

Journal of Causal Inference
Large language models (LLMs) offer the potential to automate a large number of tasks that previously have not been possible to automate, including some in science.
Huntington-Klein Nick, Murray Eleanor J.
doaj   +1 more source

Beyond Manipulation: Administrative Sorting in Regression Discontinuity Designs

Journal of Causal Inference, 2020
This paper elaborates on administrative sorting, a threat to internal validity that has been overlooked in the regression discontinuity (RD) literature.
Crespo Cristian
doaj   +1 more source

A Two-Stage Joint Modeling Method for Causal Mediation Analysis in the Presence of Treatment Noncompliance

Journal of Causal Inference, 2020
Estimating the effect of a randomized treatment and the effect that is transmitted through a mediator is often complicated by treatment noncompliance. In literature, an instrumental variable (IV)-based method has been developed to study causal mediation ...
Park Soojin, Kürüm Esra
doaj   +1 more source

The Inflation Technique Completely Solves the Causal Compatibility Problem

Journal of Causal Inference, 2020
The causal compatibility question asks whether a given causal structure graph — possibly involving latent variables — constitutes a genuinely plausible causal explanation for a given probability distribution over the graph’s observed categorical ...
Navascués Miguel, Wolfe Elie
doaj   +1 more source

Treatment effect estimation with observational network data using machine learning

Journal of Causal Inference
Causal inference methods for treatment effect estimation usually assume independent units. However, this assumption is often questionable because units may interact, resulting in spillover effects between them.
Emmenegger Corinne, Spohn Meta-Lina, Elmer Timon, Bühlmann Peter   +3 more
doaj   +1 more source

Causal structure learning in directed, possibly cyclic, graphical models

Journal of Causal Inference
We consider the problem of learning a directed graph G⋆{G}^{\star } from observational data. We assume that the distribution that gives rise to the samples is Markov and faithful to the graph G⋆{G}^{\star } and that there are no unobserved variables.
Semnani Pardis, Robeva Elina
doaj   +1 more source

Energy balancing of covariate distributions

Journal of Causal Inference
Bias in causal comparisons has a correspondence with distributional imbalance of covariates between treatment groups. Weighting strategies such as inverse propensity score weighting attempt to mitigate bias by either modeling the treatment assignment ...
Huling Jared D., Mak Simon
doaj   +1 more source