arXiv subjects

Julius Owusu

Publications and source records attributed to Julius Owusu.

6 recordsLinked to original sources

arXiv2026

Ranking Treatment Saturations under Clustered Network Interference

In this paper, we study how to rank a finite set of treatment saturations for a target population with clustered network interference. We propose an empirical success (ES) ranking rule that, for each pair of saturations, selects the saturation level with the higher estimated welfare using data from a two-stage randomized saturation design. We adopt the statistical decision theory framework with additively separable regret loss to assess the performance of the ES ranking rule. We derive non-asymptotic upper bounds on the maximum regret of the ES ranking rule that depend on the within-cluster network only through a single combinatorial summary of its dependency structure. We exploit these bounds to characterize a quasi-optimal first-stage saturation distribution within the two-stage randomized saturation design. We further show that the ES ranking rule is asymptotically optimal among threshold ranking rules in the sense of minimizing an upper bound on the worst-case regret.

econ.EM↗

arXiv2026

Causal Identification under Interference: The Role of Treatment Assignment Independence

Empirical researchers routinely invoke the no-interference or \textit{individualistic treatment response} (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary interference is present. We show that, under restrictions on dependence between treatment assignments across units, conventional ITR-based identification formulas -- including those underlying selection-on-observables, instrumental variables, regression discontinuity designs, and difference-in-differences -- identify well-defined causal objects: types of \textit{average direct effects} (ADEs). These results do not require knowledge of the interference structure or specification of exposure mappings. We also propose a sensitivity analysis framework that quantifies the robustness of statistical inference to violations of treatment-assignment independence under arbitrary interference.

econ.EM↗

arXiv2024

A Nonparametric Test of Heterogeneous Treatment Effects under Interference

Statistical inference of heterogeneous treatment effects (HTEs) across predefined subgroups is challenging when units interact because treatment effects may vary by pre-treatment variables, post-treatment exposure variables (that measure the exposure to other units' treatment statuses), or both. Thus, the conventional HTEs testing procedures may be invalid under interference. In this paper, I develop statistical methods to infer HTEs and disentangle the drivers of treatment effects heterogeneity in populations where units interact. Specifically, I incorporate clustered interference into the potential outcomes model and propose kernel-based test statistics for the null hypotheses of (i) no HTEs by treatment assignment (or post-treatment exposure variables) for all pre-treatment variables values and (ii) no HTEs by pre-treatment variables for all treatment assignment vectors. I recommend a multiple-testing algorithm to disentangle the source of heterogeneity in treatment effects. I prove the asymptotic properties of the proposed test statistics. Finally, I illustrate the application of the test procedures in an empirical setting using an experimental data set from a Chinese weather insurance program.

econ.EM↗

arXiv2023

Randomization Inference of Heterogeneous Treatment Effects under Network Interference

We develop randomization-based tests for heterogeneous treatment effects in the presence of network interference. Leveraging the exposure mapping framework, we study a broad class of null hypotheses that represent various forms of constant treatment effects in networked populations. These null hypotheses, unlike the classical Fisher sharp null, are not sharp due to unknown parameters and multiple potential outcomes. Existing conditional randomization procedures either fail to control size or suffer from low statistical power in this setting. We propose a testing procedure that constructs a data-dependent focal assignment set and permits variation in focal units across focal assignments. These features complicate both estimation and inference, necessitating new technical developments. We establish the asymptotic validity of the proposed procedure under general conditions on the test statistic and characterize the asymptotic size distortion in terms of observable quantities. The procedure is applied to experimental network data and evaluated via Monte Carlo simulations.

econ.EM↗

arXiv2023

csa2sls: A complete subset approach for many instruments using Stata

We develop a Stata command $\texttt{csa2sls}$ that implements the complete subset averaging two-stage least squares (CSA2SLS) estimator in Lee and Shin (2021). The CSA2SLS estimator is an alternative to the two-stage least squares estimator that remedies the bias issue caused by many correlated instruments. We conduct Monte Carlo simulations and confirm that the CSA2SLS estimator reduces both the mean squared error and the estimation bias substantially when instruments are correlated. We illustrate the usage of $\texttt{csa2sls}$ in Stata by an empirical application.

econ.EM↗

arXiv2022

Statistical Treatment Rules under Social Interaction

In this paper we study treatment assignment rules in the presence of social interaction. We construct an analytical framework under the anonymous interaction assumption, where the decision problem becomes choosing a treatment fraction. We propose a multinomial empirical success (MES) rule that includes the empirical success rule of Manski (2004) as a special case. We investigate the non-asymptotic bounds of the expected utility based on the MES rule. Finally, we prove that the MES rule achieves the asymptotic optimality with the minimax regret criterion.

econ.EM↗