arXiv subjects

Shunzhuang Huang

Publications and source records attributed to Shunzhuang Huang.

4 recordsLinked to original sources

arXiv2026

Inference for Treatment Effects Conditional on Generalized Principal Strata using Instrumental Variables

We propose a general approach to inference for a broad class of models that arise in the analysis of treatment effects with discrete-valued treatments and instruments and a general-valued outcome. In addition to instrument exogeneity, the main substantive assumption in our class of models rules out certain response types by assuming that they occur with probability zero. Here, the response type refers to the vector of potential outcomes and potential treatments, and we refer to a set of possible values for the response type as a generalized principal stratum. Through a series of examples, we show that this framework encompasses a wide variety of assumptions that have been considered in the previous literature. Our framework allows inference on any treatment effect parameter that can be expressed as the expectation of a function of the response type conditional on a generalized principal stratum. We develop methods for inference on such parameters under these assumptions, as well as methods for testing the validity of the assumptions themselves. A key result of our analysis is a characterization of the identified set for such parameters under these assumptions and the testable restrictions for the assumptions themselves in terms of existence of a nonnegative solution to linear systems of equations with a special structure. We propose methods for inference exploiting this special structure and recent results in Fang et al. (2023).

econ.EM↗

arXiv2025

Sharp Testable Implications of Encouragement Designs

This paper studies a potential outcome model with a continuous or discrete outcome, a discrete multi-valued treatment, and a discrete multi-valued instrument. We derive sharp, closed-form testable implications for a class of restrictions on potential treatments where each value of the instrument encourages towards at most one unique treatment choice; such restrictions serve as the key identifying assumption in several prominent recent empirical papers. Borrowing the terminology used in randomized experiments, we call such a setting an encouragement design. The testable implications are inequalities in terms of the conditional distributions of choices and the outcome given the instrument. Through a novel constructive argument, we show these inequalities are sharp in the sense that any distribution of the observed data that satisfies these inequalities is compatible with this class of restrictions on potential treatments. Based on these inequalities, we propose tests of the restrictions. In an empirical application, we show some of these restrictions are violated and pinpoint the substitution pattern that leads to the violation.

econ.EM↗

arXiv2025

On the Identifying Power of Generalized Monotonicity for Average Treatment Effects

In the context of a binary outcome, treatment, and instrument, Balke and Pearl (1993, 1997) es- tablish that the monotonicity condition of Imbens and Angrist (1994) has no identifying power beyond instrument exogeneity for average potential outcomes and average treatment effects in the sense that adding it to instrument exogeneity does not decrease the identified sets for those parameters whenever those restrictions are consistent with the distribution of the observable data. This paper shows that this phenomenon holds in a broader setting with a multi-valued outcome, treatment, and instrument, under an extension of the monotonicity condition that we refer to as generalized monotonicity. We further show that this phenomenon holds for any restriction on treatment response that is stronger than generalized monotonicity provided that these stronger restrictions do not restrict potential outcomes. Importantly, many models of potential treatments previously considered in the literature imply generalized monotonic- ity, including the types of monotonicity restrictions considered by Kline and Walters (2016), Kirkeboen et al. (2016), and Heckman and Pinto (2018), and the restriction that treatment selection is determined by particular classes of additive random utility models. We show through a series of examples that restrictions on potential treatments can provide identifying power beyond instrument exogeneity for av- erage potential outcomes and average treatment effects when the restrictions imply that the generalized monotonicity condition is violated. In this way, our results shed light on the types of restrictions required for help in identifying average potential outcomes and average treatment effects.

econ.EM↗

arXiv2025

Finite-Sample Valid Randomization Tests for Monotone Spillover Effects

Randomization tests have gained popularity for causal inference under network interference because they are finite-sample valid with minimal assumptions. However, existing procedures are limited as they primarily focus on the existence of spillovers through sharp null hypotheses on potential outcomes. In this paper, we expand the scope of randomization procedures in network settings by developing new tests for the monotonicity of spillover effects. These tests offer insights into whether spillover effects increase, decrease, or exhibit ``diminishing returns'' along certain network dimensions of interest. Our approach partitions the network into multiple (possibly overlapping) parts and tests a monotone contrast hypothesis in each sub-network. The test decisions can then be aggregated in various ways depending on how each test is constructed. We demonstrate our method by re-analyzing a large-scale policing experiment in Colombia, which reveals evidence of monotonicity related to the ``crime displacement hypothesis''. Our analysis suggests that crime spillovers on a control street increase with the number of nearby streets receiving more intense policing but diminish at higher exposure levels.

stat.ME↗