On the Foundations of the Design-Based Approach

TL;DR

This paper critically examines the foundational assumptions of the design-based approach in causal inference, proposing a generalized model that relaxes SUTVA and explores the implications for inference and research validity.

Contribution

It introduces a non-parametric, interference-allowing framework and reevaluates the role of SUTVA, highlighting the limitations of weaker assumptions like NURVA.

Findings

NURVA is insufficient for identifying key quantities.

Reconstruction of the standard paradigm clarifies the role of SUTVA.

Practical implications for applied causal inference research.

Abstract

The design-based paradigm may be adopted in causal inference and survey sampling when we assume Rubin's stable unit treatment value assumption (SUTVA) or impose similar frameworks. While often taken for granted, such assumptions entail strong claims about the data generating process. We develop an alternative design-based approach: we first invoke a generalized, non-parametric model that allows for unrestricted forms of interference, such as spillover. We define a new set of inferential targets and discuss their interpretation under SUTVA and a weaker assumption that we call the No Unmodeled Revealable Variation Assumption (NURVA). We then reconstruct the standard paradigm, reconsidering SUTVA at the end rather than assuming it at the beginning. Despite its similarity to SUTVA, we demonstrate the practical insufficiency of NURVA for identifying substantively interesting quantities. In so doing, we provide clarity on the nature and importance of SUTVA for applied research.