Results 71 to 80 of about 1,239,696 (252)

Sufficient Causes: On Oxygen, Matches, and Fires

Journal of Causal Inference, 2019
We demonstrate how counterfactuals can be used to compute the probability that one event was/is a sufficient cause of another, and how counterfactuals emerge organically from basic scientific knowledge, rather than manipulative experiments.
Pearl Judea
doaj   +1 more source

Decision-theoretic foundations for statistical causality

Journal of Causal Inference, 2021
We develop a mathematical and interpretative foundation for the enterprise of decision-theoretic (DT) statistical causality, which is a straightforward way of representing and addressing causal questions.
Dawid Philip
doaj   +1 more source

Causal Inference Meets Deep Learning: A Comprehensive Survey

Research
Deep learning relies on learning from extensive data to generate prediction results. This approach may inadvertently capture spurious correlations within the data, leading to models that lack interpretability and robustness.
Licheng Jiao, Yuhan Wang, Xu Liu, Lingling Li, Fang Liu, Wenping Ma, Yuwei Guo, Puhua Chen, Shuyuan Yang, B. Hou   +9 more
semanticscholar   +1 more source

Models, identifiability, and estimability in causal inference

, 2023
Here we discuss two common but, in our view, misguided assumptions in causal inference. The first assumption is that one requires potential outcomes, directed acyclic graphs (DAGs), or structural causal models (SCMs) for thinking about causal ...
Maclaren, Oliver John, Nicholson, Ruanui
core  

Prospective and retrospective causal inferences based on the potential outcome framework

Journal of Causal Inference
In this article, we discuss both prospective and retrospective causal inferences, building on Neyman’s potential outcome framework. For prospective causal inference, we review criteria for confounders and surrogates to avoid the Yule–Simpson paradox and ...
Geng Zhi, Zhang Chao, Wang Xueli, Liu Chunchen, Wei Shaojie   +4 more
doaj   +1 more source

Nonparametric inference for interventional effects with multiple mediators

Journal of Causal Inference, 2021
Understanding the pathways whereby an intervention has an effect on an outcome is a common scientific goal. A rich body of literature provides various decompositions of the total intervention effect into pathway-specific effects.
Benkeser David, Ran Jialu
doaj   +1 more source

Incremental intervention effects in studies with dropout and many timepoints#

Journal of Causal Inference, 2021
Modern longitudinal studies collect feature data at many timepoints, often of the same order of sample size. Such studies are typically affected by dropout and positivity violations.
Kim Kwangho, Kennedy Edward H., Naimi Ashley I.   +2 more
doaj   +1 more source

Causal Inference in Quantum Mechanics: A Reassessment [PDF]

, 2007
There has been an intense discussion, albeit largely an implicit one, concerning the inference of causal hypotheses from statistical correlations in quantum mechanics ever since John Bell’s first statement of his notorious theorem in 1966.
Suárez, Mauricio
core  

Revisiting a Discrepant Result: A Propensity Score Analysis, the Paired Availability Design for Historical Controls, and a Meta-Analysis of Randomized Trials

Journal of Causal Inference, 2013
There is an ongoing controversy over whether epidural analgesia for women in labor increases the probability of Caesarean section. Previous research compared results from three methods for estimating the effect of epidural analgesia on the probability of
G. Baker Stuart, S. Lindeman Karen
doaj   +1 more source

Conditional as-if analyses in randomized experiments

Journal of Causal Inference, 2021
The injunction to “analyze the way you randomize” is well known to statisticians since Fisher advocated for randomization as the basis of inference. Yet even those convinced by the merits of randomization-based inference seldom follow this injunction to ...
Pashley Nicole E., Basse Guillaume W., Miratrix Luke W.   +2 more
doaj   +1 more source