The Taboo Against Explicit Causal Inference in Nonexperimental Psychology

Abstract

Causal inference is a central goal of research. However, most psychologists refrain from explicitly addressing causal research questions and avoid drawing causal inference on the basis of nonexperimental evidence. We argue that this taboo against causal inference in nonexperimental psychology impairs study design and data analysis, holds back cumulative research, leads to a disconnect between original findings and how they are interpreted in subsequent work, and limits the relevance of nonexperimental psychology for policymaking. At the same time, the taboo does not prevent researchers from interpreting findings as causal effects—the inference is simply made implicitly, and assumptions remain unarticulated. Thus, we recommend that nonexperimental psychologists begin to talk openly about causal assumptions and causal effects. Only then can researchers take advantage of recent methodological advances in causal reasoning and analysis and develop a solid understanding of the underlying causal mechanisms that can inform future research, theory, and policymakers.

ANCOVA versus Change Score for the Analysis of Nonexperimental Two-Wave Data: A Structural Modeling Perspective

“…By clarifying the causal assumptions of these two widely used approaches, we hope to support researchers in utilizing the strength of longitudinal data and, at the same time, we hope to raise the awareness of the strong assumptions that are needed for making causal conclusions. This is also in line with recent prominent calls in psychology that nonexperimental research should begin to talk more openly about causal assumptions and causal effects because causal questions are central to most psychological theories (e.g., Brick & Bailey, 2020;Foster, 2010;Grosz, Rohrer, & Thoemmes, 2020;Quynh Nguyen, Schmid, & Stuart, 2020).…”

That’s a lot to Process! Pitfalls of Popular Path Models

“…And it only makes sense to talk about and interpret mediation from a causal perspective; from a strictly statistical perspective the phenomenon is indistinguishable from confounding (MacKinnon et al, 2000) . To make the most of causal inference on the basis of observational data, it is best to take the bull by the horns while remaining transparent about the underlying assumptions, rather than resorting to ambiguous language that obscures the goal of the analysis (Grosz et al, 2020) . Somewhat ironically, explicit causal language may prompt readers to be more careful when evaluating whether conclusions are appropriate (Alvarez-Vargas et al, 2020) .…”

“…For example, we may notice that open-ended exploration, description (Rozin, 2001) or prediction (Yarkoni & Westfall, 2017) are more suitable endeavors for the matter at hand; or that more basic questions regarding measurement need to be settled first. All of these types of investigations, if conducted rigorously, are relevant scientific contributions in their own right-researchers should not feel pressured to disguise them as hypothesis-testing confirmatory studies making some (explicit or implicit; Grosz et al, 2020) causal claim.…”

Descriptive, Predictive and Explanatory Personality Research: Different Goals, Different Approaches, but a Shared Need to Move beyond the Big Few Traits

“…Instead, useful explanations for these patterns could postulate general principles that may or may not apply to potentially controllable processes in particular individuals. We now elaborate on this position, because we feel that it is implicitly adopted by many personality researchers but may cause unrealistic expectations when left unarticulated (Grosz, Rohrer, & Thoemmes, 2020). We will later return to the alternative view according to which personality researchers should hope to reveal the individual causes of personality‐relevant phenomena in the strict sense of the term.…”