Stacked Triple Differences

TL;DR

The paper introduces stacked DDD, a new regression-based method for difference-in-differences analysis under staggered adoption, improving interpretability and flexibility in estimating treatment effects.

Contribution

It extends the difference-in-differences approach to the DDD setting by creating self-contained stacks, enabling transparent, regression-based causal inference with alternative weighting schemes.

Findings

Stacked DDD identifies weighted average treatment effects at each post-treatment time.

Alternative weighting schemes recover causal estimands with clear interpretations.

Empirical illustrations show stacked DDD can yield different conclusions from existing methods.

Abstract

Triple differences (DDD) is a workhorse quasi-experimental design in applied economics. But, under staggered adoption, its conventional three-way fixed-effects (3WFE) implementation inherits the interpretation issues now well understood in the difference-in-differences literature. I introduce stacked DDD. I extend the stacked difference-in-differences approach to the DDD setting by creating self-contained stacks, each consisting of four cells over an event window: treated and clean comparison cohorts, each with treatment-eligible and treatment-ineligible units. Appending these stacks yields a unified dataset for estimating treatment effects. I prove that, at each post-treatment event-time, a linear regression with fully saturated fixed-effects applied to the stacked dataset identifies a strictly positive, cell-size-weighted average of stack-level conditional average treatment effects, with stack weights proportional to stack-level cell sizes. Building on this characterization, I outline alternative weighting schemes that recover causal estimands with clear interpretations. Stacked DDD complements recent GMM and imputation-based frameworks by trading efficiency for regression-based transparency, pairwise (rather than global) parallel changes-in-trends, and direct control over both the comparison group for each treated unit and the aggregation weights. I provide two empirical illustrations where stacked DDD yields substantially different quantitative conclusions compared to existing procedures.