Interference and Spillover Effects

Causal Inference
Interference
Project Lab
An applied causal inference lab on exposure mapping, spillovers, and direct-indirect effect decomposition.
Total effect decomposition under spillovers
Figure 1: Total effect decomposition separating direct exposure effects from spillover pathways.

This lab studies settings where one unit’s treatment can affect another unit’s outcome. That matters in recommendation, marketplace, and networked systems because exposure can propagate through shared items, users, clusters, or local neighborhoods.

The workflow starts by defining exposure rather than assuming independent units. It then compares naive estimators with exposure-aware designs, separates direct and indirect effects, and studies how conclusions change when the spillover definition changes.

Lab Sequence

03. Cluster Randomized Estimators

Uses clustered assignment logic to estimate effects when correlated exposure and spillover risk make individual assignment less credible.

05. Advanced Spillover Models

Adds richer modeling and targeting analyses to study where spillovers are strongest and how policy value changes under network-aware estimation.