Timezone: »

 
Oral
Learning Treatment Effects in Panels with General Intervention Patterns
Vivek Farias · Andrew Li · Tianyi Peng

Fri Dec 10 04:40 PM -- 04:55 PM (PST) @
in Oral Session 5: Generative Modeling »
The problem of causal inference with panel data is a central econometric question. The following is a fundamental version of this problem: Let $M^*$ be a low rank matrix and $E$ be a zero-mean noise matrix. For a `treatment' matrix $Z$ with entries in $\{0,1\}$ we observe the matrix $O$ with entries $O_{ij} := M^*_{ij} + E_{ij} + \mathcal{T}_{ij} Z_{ij}$ where $\mathcal{T}_{ij} $ are unknown, heterogenous treatment effects. The problem requires we estimate the average treatment effect $\tau^* := \sum_{ij} \mathcal{T}_{ij} Z_{ij} / \sum_{ij} Z_{ij}$. The synthetic control paradigm provides an approach to estimating $\tau^*$ when $Z$ places support on a single row. This paper extends that framework to allow rate-optimal recovery of $\tau^*$ for general $Z$, thus broadly expanding its applicability. Our guarantees are the first of their type in this general setting. Computational experiments on synthetic and real-world data show a substantial advantage over competing estimators.
Keywords: Causality

Author Information

Vivek Farias (Massachusetts Institute of Technology)
Andrew Li (Carnegie Mellon University)
Tianyi Peng (Massachusetts Institute of Technology)

Related Events (a corresponding poster, oral, or spotlight)

More from the Same Authors