Simula@BI: Adaptive Partial Identification of Treatment Effects

This paper identifies and estimates heterogeneous treatment effect bounds based on the monotonic treatment selection and response assumptions.

Specifically, we propose multivariate random forests that can be used to fit the bounds of treatment effects identified as the solution to a set of local moment equations. To detect heterogeneous subgroups, multivariate random forests adaptively search for subsets of data that exhibit the highest variation in the treatment effect bounds.

The large sample theory in this paper shows that the method recovers consistent and asymptotically normally distributed parameters. Simulation experiments and applications on the National Longitudinal Survey of Youth and U.S. Census data suggest significant heterogeneity in the effect of the Head Start program on years of schooling, and in the effect of the family size on the mother's labor supply.

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