Results 31 to 40 of about 125 (80)

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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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
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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
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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
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Bias formulas for violations of proximal identification assumptions in a linear structural equation model

Journal of Causal Inference
Causal inference from observational data often rests on the unverifiable assumption of no unmeasured confounding. Recently, Tchetgen Tchetgen and colleagues have introduced proximal inference to leverage negative control outcomes and exposures as proxies
Cobzaru Raluca, Welsch Roy, Finkelstein Stan, Ng Kenney, Shahn Zach   +4 more
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Multivariate zero-inflated causal model for regional mobility restriction effects on consumer spending

Journal of Causal Inference
The COVID-19 pandemic presents challenges to both public health and the economy. Our objective is to examine how household expenditure, a significant component of private demand, reacts to changes in mobility. This investigation is crucial for developing
Hong Taekwon, Lu Wenbin, Yang Shu, Ghosh Pulak   +3 more
doaj   +1 more source