|
|
While there now exists a large literature
on policy evaluation and learning, much of prior work assumes that
the treatment assignment of one unit does not affect the outcome of
another unit. Unfortunately, ignoring interference may lead to
biased policy evaluation and yield ineffective learned policies. For
example, treating influential individuals who have many friends can
generate positive spillover effects, thereby improving the overall
performance of an individualized treatment rule (ITR). We consider
the problem of evaluating and learning an optimal ITR under
clustered network (or partial) interference where clusters of units
are sampled from a population and units may influence one another
within each cluster. Under this model, we propose an estimator that
can be used to evaluate the empirical performance of an ITR. We show
that this estimator is substantially more efficient than the
standard inverse probability weighting estimator, which does not
impose any assumption about spillover effects. We derive the
finite-sample regret bound for a learned ITR, showing that the use
of our efficient evaluation estimator leads to the improved
performance of learned policies. Finally, we conduct simulation and
empirical studies to illustrate the advantages of the proposed
methodology. |
Imai, Kosuke, Zhichao Jiang, and Anup
Malani. (2021). ``Causal
Inference with Interference and Noncompliance in Two-Stage
Randomized Experiments.'' Journal of the
American Statistical Association, Vol. 116, No. 534,
pp. 632-644. |
Jiang, Zhichao, Kosuke Imai, and Anup
Malani. (2023). ``Statistical
Inference and Power Analysis for Direct and Spillover Effects in
Two-Stage Randomized Experiments.''
Biometrics, Vol. 79, No. 3 (September), pp. 2370-2381.
|
Chattopadhyay, Ambarish, Kosuke Imai, and
Jose R. Zubizarreta. ``Design-based inference for generalized
network experiments with stochastic interventions.''
|
Imai, Kosuke and Michael Lingzhi
Li. (2023). ``Experimental
Evaluation of Individualized Treatment Rules.''
Journal of the American Statistical Association,
Vol. 118, No. 541, pp. 242-256. |