Results 21 to 30 of about 125 (80)

On the pitfalls of Gaussian likelihood scoring for causal discovery

Journal of Causal Inference, 2023
We consider likelihood score-based methods for causal discovery in structural causal models. In particular, we focus on Gaussian scoring and analyze the effect of model misspecification in terms of non-Gaussian error distribution. We present a surprising
Schultheiss Christoph, Bühlmann Peter
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An optimal transport approach to estimating causal effects via nonlinear difference-in-differences

Journal of Causal Inference
We 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, Gunsilius Florian, Rigollet Philippe   +2 more
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Mediated probabilities of causation

Journal of Causal Inference
We propose a set of causal estimands that we call “the mediated probabilities of causation.” These estimands quantify the probabilities that an observed negative outcome was induced via a mediating pathway versus a direct pathway in a stylized setting ...
Rubinstein Max, Cuellar Maria, Malinsky Daniel   +2 more
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Quantifying the quality of configurational causal models

Journal of Causal Inference
There is a growing number of studies benchmarking the performance of configurational comparative methods (CCMs) of causal data analysis. A core benchmark criterion used in these studies is a dichotomous (i.e., non-quantitative) correctness criterion ...
Baumgartner Michael, Falk Christoph
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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.
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Heterogeneous interventional effects with multiple mediators: Semiparametric and nonparametric approaches

Journal of Causal Inference, 2023
We propose semiparametric and nonparametric methods to estimate conditional interventional indirect effects in the setting of two discrete mediators whose causal ordering is unknown. Average interventional indirect effects have been shown to decompose an
Rubinstein Max, Branson Zach, Kennedy Edward H.   +2 more
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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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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
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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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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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