Quantitative analysis of causal effects in political science has trended toward the adoption of "causal empiricist" approaches. Such approaches place heavy emphasis on causal identification through experimental and natural experimental designs and on characterizing the specific subpopulations...

Either in the form of nature’s wrath or a pandemic, catastrophes cause major destructions in societies, thus requiring policy and decision makers to take urgent action by evaluating a host of interdependent parameters, and possible scenarios. The primary purpose of this pa-per is to propose a...

Brodeur et al. (2020) study a large number of hypothesis tests from top economic articles and find evidence for p-hacking or publication bias, in particular for articles using DID or IV as causal identification method. We show that a crucial continuity assumption is violated because many...

Introduction/reflections -- Authors -- Introduction to the PROACT root cause analysis (RCA) work process -- Introduction to the field of root cause analysis -- Creating the environment for RAC to succeed: the reliability performance process (TRPP) -- Failure classification -- Opportunity...

Economists are increasingly turning to the experimental method as a means to estimate causal effects. By using randomization to identify key treatment effects, theories previously viewed as untestable are now scrutinized, efficacy of public policies are now more easily verified, and stakeholders...

This article deals with the role of time in causal models in the social sciences, in particular in structural causal modeling, in contrast to time-free models. The aim is to underline the importance of time-sensitive causal models. For this purpose, it also refers to the important discussion on...