Making sense of sensitivity: Extending omitted variable bias
We extend the omitted variable bias framework with a suite of tools for sensitivity analysis in
regression models that does not require assumptions on the functional form of the treatment�…
regression models that does not require assumptions on the functional form of the treatment�…
Quantifying the robustness of causal inferences: Sensitivity analysis for pragmatic social science
Social scientists seeking to inform policy or public action must carefully consider how to
identify effects and express inferences because actions based on invalid inferences may not�…
identify effects and express inferences because actions based on invalid inferences may not�…
[HTML][HTML] A unified framework of longitudinal models to examine reciprocal relations.
Inferring reciprocal effects or causality between variables is a central aim of behavioral and
psychological research. To address reciprocal effects, a variety of longitudinal models that�…
psychological research. To address reciprocal effects, a variety of longitudinal models that�…
Mediation analysis
M Huber�- Handbook of labor, human resources and population�…, 2020 - Springer
Mediation analysis aims at evaluating the causal mechanisms through which a treatment or
intervention affects an outcome of interest. The goal is to disentangle the total treatment�…
intervention affects an outcome of interest. The goal is to disentangle the total treatment�…
sensemakr: Sensitivity analysis tools for OLS in R and Stata
This paper introduces the package sensemakr for R and Stata, which implements a suite of
sensitivity analysis tools for regression models developed in Cinelli and Hazlett (2020a)�…
sensitivity analysis tools for regression models developed in Cinelli and Hazlett (2020a)�…
Causal moderated mediation analysis: Methods and software
Research questions regarding how, for whom, and where a treatment achieves its effect on
an outcome have become increasingly valued in substantive research. Such questions can�…
an outcome have become increasingly valued in substantive research. Such questions can�…
Insights into the cross-world independence assumption of causal mediation analysis
RM Andrews, V Didelez�- Epidemiology, 2021 - journals.lww.com
Causal mediation analysis is a useful tool for epidemiologic research, but it has been
criticized for relying on a “cross-world” independence assumption that counterfactual�…
criticized for relying on a “cross-world” independence assumption that counterfactual�…
[HTML][HTML] When less conditioning provides better estimates: overcontrol and endogenous selection biases in research on intergenerational mobility
M Gr�tz�- Quality & Quantity, 2022 - Springer
The counterfactual approach to causality has become the dominant approach to understand
causality in contemporary social science research. Whilst most sociologists are aware that�…
causality in contemporary social science research. Whilst most sociologists are aware that�…
Adjusting for baseline measurements of the mediators and outcome as a first step toward eliminating confounding biases in mediation analysis
Mediation analysis prevails for researchers probing the etiological mechanisms through
which treatment affects an outcome. A central challenge of mediation analysis is justifying�…
which treatment affects an outcome. A central challenge of mediation analysis is justifying�…
Simulation-based sensitivity analysis for causal mediation studies.
Causal inference regarding a hypothesized mediation mechanism relies on the assumptions
that there are no omitted pretreatment confounders (ie, confounders preceding the�…
that there are no omitted pretreatment confounders (ie, confounders preceding the�…