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Dustin Tingley

Researcher at Harvard University

Publications -  146
Citations -  17940

Dustin Tingley is an academic researcher from Harvard University. The author has contributed to research in topics: Politics & Foreign policy. The author has an hindex of 43, co-authored 142 publications receiving 13700 citations. Previous affiliations of Dustin Tingley include University of Chicago & Princeton University.

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mediation: R Package for Causal Mediation Analysis

TL;DR: The mediation package implements a comprehensive suite of statistical tools for conducting causal mediation analysis in applied empirical research and implements a statistical method for dealing with multiple (causally dependent) mediators, which are often encountered in practice.
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A general approach to causal mediation analysis.

TL;DR: The approach is general because it offers the definition, identification, estimation, and sensitivity analysis of causal mediation effects without reference to any specific statistical model and can accommodate linear and nonlinear relationships, parametric and nonparametric models, continuous and discrete mediators, and various types of outcome variables.
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Redefine statistical significance

Daniel J. Benjamin, +76 more
TL;DR: The default P-value threshold for statistical significance is proposed to be changed from 0.05 to 0.005 for claims of new discoveries in order to reduce uncertainty in the number of discoveries.
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Redefine Statistical Significance

TL;DR: This article proposed to change the default P-value threshold for statistical significance for claims of new discoveries from 0.05 to 0.005, which is the threshold used in this paper.
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Unpacking the Black Box of Causality: Learning about Causal Mechanisms from Experimental and Observational Studies

TL;DR: This work presents a minimum set of assumptions required under standard designs of experimental and observational studies and develops a general algorithm for estimating causal mediation effects and provides a method for assessing the sensitivity of conclusions to potential violations of a key assumption.