Improved Guarantees for Heterogeneous Treatment-Effect Estimation via Matrix Completion

A central goal of modern causal inference is estimating heterogeneous treatment effects to answer questions like “how does an intervention affect each unit,” rather than only on average. We study this problem with panel-data where we observe $n$ units across $m$ times under unknown, non-uniform treatment assignments. The data in this setting is naturally represented as a matrix of all unit–time treatment effects. Estimating heterogeneous treatment effects can then be expressed as obtaining a good estimation of each row's average in this matrix. This allows us to formulate the problem as matrix completion, which can be solved under natural low-rankness assumptions. However, existing matrix-completion guarantees are not powerful enough to get meaningful bounds for the per-row guarantee required for estimating the heterogeneous treatment effect; roughly speaking, they are only useful for estimating average treatment effect bounds, as also illustrated in a recent line of work. We give a simple, computationally efficient estimator that, without knowledge of the propensities and under standard low-rankness and regularity assumptions, achieves a row-wise ${\ell }_2$ error of $\tilde{O}(\sqrt{\frac{1}{n}+\frac{n}{(m^2)}})$. Technically, our analysis establishes the first sharp row-wise ${\ell }_2$-perturbation bound for low-rank approximation, complementing existing spectral-, Frobenius-, and entrywise perturbation theory. We evaluate our estimator on both synthetic and semi-synthetic data, and find lower estimation error than direct Horvitz–Thompson and Hájek estimators for several unit-specific treatment effects. Further, in synthetic experiments, the error decay matches the dependence on panel size predicted by our theoretical bounds.