Results 21 to 30 of about 137 (85)

Estimating causal effects with the neural autoregressive density estimator

Journal of Causal Inference, 2021
The estimation of causal effects is fundamental in situations where the underlying system will be subject to active interventions. Part of building a causal inference engine is defining how variables relate to each other, that is, defining the functional
Garrido Sergio, Borysov Stanislav, Rich Jeppe, Pereira Francisco   +3 more
doaj   +1 more source

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
doaj   +1 more source

On the bias of adjusting for a non-differentially mismeasured discrete confounder

Journal of Causal Inference, 2021
Biological and epidemiological phenomena are often measured with error or imperfectly captured in data. When the true state of this imperfect measure is a confounder of an outcome exposure relationship of interest, it was previously widely believed that ...
Peña Jose M., Balgi Sourabh, Sjölander Arvid, Gabriel Erin E.   +3 more
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Bias attenuation results for dichotomization of a continuous confounder

Journal of Causal Inference, 2022
It is well-known that dichotomization can cause bias and loss of efficiency in estimation. One can easily construct examples where adjusting for a dichotomized confounder causes bias in causal estimation.
Gabriel Erin E., Peña Jose M., Sjölander Arvid   +2 more
doaj   +1 more source

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
doaj   +1 more source

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
doaj   +1 more source

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

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
doaj   +1 more source