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Generalizing Experimental Findings
Journal of Causal Inference, 2015 This note examines one of the most crucial questions in causal inference: “How generalizable are randomized clinical trials?” The question has received a formal treatment recently, using a non-parametric setting, and has led to a simple and general ...
Pearl Judea
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, 2015 Causal inference deals with identifying which random variables "cause" or control other random variables. Recent advances on the topic of causal inference based on tools from statistical estimation and machine learning have resulted in practical algorithms for causal inference.
Matt J. Kusner +3 more
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FAST RESTRICTED CAUSAL INFERENCE [PDF]
Demonstratio Mathematica, 2000 Hidden variables are well known sources of disturbance when recovering belief networks from data based only on measurable variables. Hence models assuming existence of hidden variables are under development. This paper presents a new algorithm "accelerating" the known CI algorithm of Spirtes, Glymour and Scheines {Spirtes:93}.
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y0-causal-inference/y0: v0.2.6
<h2>What's Changed</h2> <ul> <li>Improve variable sort and CF graph testing by @cthoyt in https://github.com/y0-causal-inference/y0/pull/199</li> <li>Use frozensets for interventions by @cthoyt in https://github.com/y0-
Jeremy Zucker +4 more
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Journal of Causal Inference, 2017 1 In this paper, we study the effect of a time-varying exposure mediated by a time-varying intermediate variable. We consider general longitudinal settings, including survival outcomes.
Zheng Wenjing, van der Laan Mark
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y0-causal-inference/y0: v0.2.7
<h2>What's Changed</h2> <ul> <li>Add SCM parameter estimation by @cthoyt in https://github.com/y0-causal-inference/y0/pull/201</li> </ul> <p><strong>Full Changelog</strong>: https://github.com/y0 ...
Jeremy Zucker +4 more
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Data-Adaptive Causal Effects and Superefficiency
Journal of Causal Inference, 2016 Recent approaches in causal inference have proposed estimating average causal effects that are local to some subpopulation, often for reasons of efficiency.
Aronow Peter M.
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Comparing families of dynamic causal models [PDF]
, 2010 Mathematical models of scientific data can be formally compared using Bayesian model evidence. Previous applications in the biological sciences have mainly focussed on model selection in which one first selects the model with the highest evidence and ...
Stephan, Klaas E +42 more
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Sensitivity analysis for causal effects with generalized linear models
, 2022 Residual confounding is a common source of bias in observational studies. In this article, we build upon a series of sensitivity analyses methods for residual confounding developed by Brumback et al. and Chiba whose sensitivity parameters are constructed
Gabriel, EE +5 more
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Physical and Metaphysical Counterfactuals: Evaluating Disjunctive Actions
Journal of Causal Inference, 2017 The structural interpretation of counterfactuals as formulated in Balke and Pearl (1994a,b) [1, 2] excludes disjunctive conditionals, such as “had X$X$ been x1 or x2$x_1~\mbox{or}~x_2$,” as well as disjunctive actions such as do(X=x1 or X=x2)$do(X=x_1 ...
Pearl Judea
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