This primer systematizes the emerging literature on causal inference using deep neural networks under the potential outcomes framework. It provides an intuitive introduction to building and optimizing custom deep learning models and shows how to adapt them to estimate/predict heterogeneous treatment effects. It also discusses ongoing work to…

Previous research explicates ordinal and disordinal interactions through the concept of the "crossover point." This point is determined via simple regression models of a focal predictor at specific moderator values and signifies the intersection of these models. An interaction effect is labeled as disordinal (or ordinal) when the…

Conventional approaches to causation in the social sciences draw on approaches in the Philosophy of Science in which a causal force acts on cases and generates change in the form of events. This relies on just one of the Aristotelian conceptions of cause - efficient cause - what brings the effect in to being. We should also pay attention to Final…

Causal-formative indicators are often used in social science research. To achieve identification in causal-formative indicator modeling, constraints need to be applied. A conventional method is to constrain the weight of a formative indicator to be 1. The selection of which indicator to have the fixed weight, however, may influence statistical…

Fixed effects (FE) panel models have been used extensively in the past, as those models control for all stable heterogeneity between units. Still, the conventional FE estimator relies on the assumption of parallel trends between treated and untreated groups. It returns biased results in the presence of heterogeneous slopes or growth curves that…

Comparative and case study researchers have responded to critiques of their methods by developing formal procedures to validate theoretical claims through set theoretical logics of causal conditions. This 'logico-formalist turn' has involved the stricter application of the schemas of set theory and the philosophy of logic to raise validation…

Quasi-experimental methods are a cornerstone of applied social science, providing critical answers to causal questions that inform policy and practice. Although open science principles have influenced experimental research norms across the social sciences, these practices are rarely implemented in quasi-experimental research. In this paper, we…

Qualitative Comparative Analysis (QCA) is a relatively young method of causal inference that continues to diffuse across the social sciences. However, recent methodological research has found the conservative (QCA-CS) and the intermediate solution type (QCA-IS) of QCA to fail fundamental tests of correctness. Even under conditions otherwise ideal…

Even though agent-based modelling is seen as committing to a mechanistic, generative type of causation, the methodology allows for representing many other types of causal explanations. Agent-based models are capable of "integrating" diverse causal relationships into coherent causal mechanisms. They mirror the crucial, multi-level…

Does "X" affect "Y"? Answering this question is particularly difficult if reverse causality is looming. Many social scientists turn to panel data to address such questions of causal ordering. Yet even in longitudinal analyses, reverse causality threatens causal inference based on conventional panel models. Whereas the…

In the complex world of educational policy, causal inferences will be debated. As we review non-experimental designs in educational policy, we focus on how to clarify and focus the terms of debate. We begin by presenting the potential outcomes/counterfactual framework and then describe approximations to the counterfactual generated from the…

One of the core dimensions of historical and social science inquiry is "[d]eveloping questions and planning inquiries" according to the National Council for the Social Studies' College, Career, and Civic Life (C3) Framework. Yet existing education research, instructional resources, and assessment have ignored how students frame problems…

Conventional advice discourages controlling for postoutcome variables in regression analysis. By contrast, we show that controlling for commonly available postoutcome (i.e., future) values of the treatment variable can help detect, reduce, and even remove omitted variable bias (unobserved confounding). The premise is that the same unobserved…

Randomized control trials are considered the pinnacle for causal inference. In many cases, however, randomization of participants in social work research studies is not feasible or ethical. This paper introduces the co-twin control design study as an alternative quasi-experimental design to provide evidence of causal mechanisms when randomization…

Mediation analysis plays an important role in understanding causal processes in social and behavioral sciences. While path analysis with composite scores was criticized to yield biased parameter estimates when variables contain measurement errors, recent literature has pointed out that the population values of parameters of latent-variable models…