Results 51 to 60 of about 117 (76)

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

Estimating population average treatment effects from experiments with noncompliance

Journal of Causal Inference, 2020
Randomized control trials (RCTs) are the gold standard for estimating causal effects, but often use samples that are non-representative of the actual population of interest.
Ottoboni Kellie N., Poulos Jason V.
doaj   +1 more source

Identification and Estimation of Intensive Margin Effects by Difference-in-Difference Methods

Journal of Causal Inference, 2020
This paper discusses identification and estimation of causal intensive margin effects. The causal intensive margin effect is defined as the treatment effect on the outcome of individuals with a positive outcome irrespective of whether they are treated or
Hersche Markus, Moor Elias
doaj   +1 more source

Causal Structure Learning with Conditional and Unique Information Groups-Decomposition Inequalities [PDF]

The causal structure of a system imposes constraints on the joint probability distribution of variables that can be generated by the system. Archetypal constraints consist of conditional independencies between variables.
Chicharro, D., Nguyen, J. K.
core   +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

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
doaj   +1 more source

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

A clarification on the links between potential outcomes and do-interventions

Journal of Causal Inference
Most of the scientific literature on causal modeling considers the structural framework of Pearl and the potential-outcome framework of Rubin to be formally equivalent and therefore interchangeably uses do-interventions and the potential-outcome ...
De Lara Lucas
doaj   +1 more source

Generalized coarsened confounding for causal effects: a large-sample framework

Journal of Causal Inference
There has been widespread use of causal inference methods for the rigorous analysis of observational studies and to identify policy evaluations. In this article, we consider a class of generalized coarsened procedures for confounding.
Ghosh Debashis, Wang Lei
doaj   +1 more source

Current philosophical perspectives on drug approval in the real world

Journal of Causal Inference
The evidence-based medicine approach to causal medical inference is the dominant account among medical methodologists. Competing approaches originating in the philosophy of medicine seek to challenge this account.
Landes Jürgen, Auker-Howlett Daniel J.
doaj   +1 more source