Embarrassingly Causal: Causal Use of Associational Data in Magic The Gathering Drafts

TL;DR

This paper introduces the concept of embarrassingly causal scenarios where observational data can reliably inform causal inferences, demonstrated through Magic The Gathering draft analysis.

Contribution

It defines embarrassingly causal scenarios and shows how observational data can be used for causal inference in Magic The Gathering without strong assumptions.

Findings

Observational data effectively guides draft choices despite confounding.

Embarrassingly causal scenarios justify causal estimand construction from observational data.

Guidance provided for evaluating causal inference assumptions in observational studies.

Abstract

Observational data are often used to answer causal questions, yet the legitimacy of doing so is often argued to hinge on strong, domain supported assumptions about underlying causal structure with limited guidance on how much domain knowledge support should exist to justify including a causal edge of interest in a directed acyclic graph. We introduce the criterion of embarrassingly causal scenarios, where the existence of an exposure outcome relationship is so uncontroversial that the assumptions needed to include the corresponding causal edge in a DAG can be reasonably made. Using the case of Magic The Gathering booster draft decisions and gameplay outcomes, we show how purely observational data from 17Lands are widely and effectively used to guide draft choices despite substantial confounding, selection effects, and post treatment conditioning. We argue that the embarrassingly causal quality is a sufficient condition for justifying the construction of causal estimands and the collection of observational data to estimate them. Correspondingly, we provide guidance on evaluating observational causal inference assumptions for authors, reviewers, and readers.