Explaining Causal Findings Without Bias: Detecting and Assessing Direct Effects

By Avidit Acharya, M. Blackwell, M. Sen. American Political Science Review, 2016

461 citations recorded by the source corpus.

Subfields: Demography, Sociology and Political Science, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/3c117cef9c44e85fc619ad27db8fd37a7de58149

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Citations in the corpus

Does Institutional Quality Foster Economic Complexity? (2019, unknown): “However, this widely adopted method yields inconsistent estimates due to intermediate variables bias (Acharya et al., 2016).”
Impact of Violent Crime on Risk Aversion: Evidence from the Mexican Drug War (2019, unknown): “We adopt the mediation procedure outlined in Acharya, Blackwell, and Sen (2016) and compare estimates of the average controlled direct effect (ACDE) with our main result to provide a sense of how much of the effect of violence on risk aversion can be explained by different sets of mediators.”
The Long-Run Impacts of Same-Race Teachers (2017, unknown): “” (24)The mediation analysis is prone to bias caused by what Acharya et al. (2016) call intermediate confounders and what Imai et al. (2010) call the failure of sequential ignorability: the mediators (absences and achievement) are themselves potentially affected by other unobserved mediators.”
No Harm in Checking: Using Factual Manipulation Checks to Assess Attentiveness in Experiments (2018, background): “…we acknowledge that some researchers use FMCs to conduct analyses on “passers” only, this practice raises concerns regarding posttreatment bias (e.g., Acharya, Blackwell, and Sen 2016; Montgomery, Nyhan, and Torres 2018) and/or unrepresentative samples (Berinsky, Margolis, and Sances 2014,…”
The causal interpretation of estimated associations in regression models (2019, background): “(2011), Acharya et al. (2016), and Van der Weele (2015) for more details on indirect and direct effects.”
How Conditioning on Posttreatment Variables Can Ruin Your Experiment and What to Do about It (2018, background): “…recognized in the fields of statistics, econometrics, and political methodology (e.g., Rosenbaum 1984; Wooldridge 2005; King and Zeng 2006; Elwert and Winship 2014; Acharya, Blackwell, and Sen 2016), there is still significant confusion in the wider discipline about its sources and consequences.”