Subjective Causality and Counterfactuals in the Social Sciences: Toward an Ethnographic Causality?

Abstract

The article explores the role that subjective evidence of causality and associated counterfactuals and counterpotentials might play in the social sciences where comparative cases are scarce. This scarcity rules out statistical inference based upon frequencies and usually invites in-depth ethnographic studies. Thus, if causality is to be preserved in such situations, a conception of ethnographic causal inference is required. Ethnographic causality inverts the standard statistical concept of causal explanation in observational studies, whereby comparison and generalization, across a sample of cases, are both necessary prerequisites for any causal inference. Ethnographic causality allows, in contrast, for causal explanation prior to any subsequent comparison or generalization.

Causal inference in ethnographic research: Refining explanations with abductive logic, strength of evidence assessments, and graphical models

“…By contrast, our ethnographic analysis in both our past and more recent work does share features with the constant comparison techniques of analytic induction and grounded theory [ 15 , 17 , 18 ]: we meticulously documented and analyzed how different ways of relating to an avatar (coded as themes ) were associated with other factors (other themes); and we aimed to clarify through this process how certain factors (like player-avatar relationships) might causally impact others (gaming-related wellbeing). Indeed, some of those presumably largely inductive approaches explicitly incorporate a counterfactual component, as do we, by selecting cases for comparison that vary along some key dimension that allows researchers to refine causal models by ruling out alternate explanations [ 13 , 14 , 97 ]. Logical deduction also played a role in our analysis, e.g., in how we reasoned deductively from what we knew to theoretically sample new cases for analysis.…”

The Subjective Causal Impact of COVID-19 on Graduate Medical Education and Recommendations for Bridging the Educational Gap: A Global Cross-sectional Study

“…From the collected data, we directly calculate the residentlevel subjective treatment effect. Our approach builds on a growing literature that uses subjective expectations to understand decision-making under uncertainty [32,33,34,35]. The soundness of our approach relies on the key assumption that residents have well-formed expectations for outcomes in both the realized and the counterfactual state.…”