Treatment Effect Models with Strategic Interaction in Treatment Decisions

This study considers treatment effect models in which others' treatment decisions can affect one's own treatment and outcome. Focusing on the case of two-player interactions, we formulate treatment decision behavior as a complete information game with multiple equilibria. Using a latent index framework and assuming a stochastic equilibrium selection, we prove that the marginal treatment effect from one's own treatment and that from the partner can be identified separately. Based on our constructive identification results, we propose a two-step semiparametric procedure for estimating the marginal treatment effects using series approximation. We show that the proposed estimator is uniformly consistent and asymptotically normally distributed. As an empirical illustration, we investigate the impacts of risky behaviors on adolescents' academic performance

Doubly Robust Difference-in-Differences with General Treatment Patterns

We develop a difference-in-differences method in a general setting in which the treatment variable of interest may be non-binary and its value may change in each time period. It is generally difficult to estimate treatment parameters defined with the potential outcome given the entire path of treatment adoption, as each treatment path may be experienced by only a small number of observations. We propose an empirically tractable alternative using the concept of effective treatment, which summarizes the treatment path into a low-dimensional variable. Under a parallel trends assumption conditional on observed covariates, we show that doubly robust difference-in-differences estimands can identify certain average treatment effects for movers, even when the chosen effective treatment is misspecified. We consider doubly robust estimation and multiplier bootstrap inference, which are asymptotically justifiable if either an outcome regression function for stayers or a generalized propensity score is correctly parametrically specified. We illustrate the usefulness of our method by estimating the instantaneous and dynamic effects of union membership on wages

Causal Inference with Noncompliance and Unknown Interference

We consider a causal inference model in which individuals interact in a social network and they may not comply with the assigned treatments. Estimating causal parameters is challenging in the presence of network interference of unknown form, as each individual may be influenced by both close individuals and distant ones in complex ways. Noncompliance with treatment assignment further complicates this problem, and prior methods dealing with network spillovers but disregarding the noncompliance issue may underestimate the effect of the treatment receipt on the outcome. To estimate meaningful causal parameters, we introduce a new concept of exposure mapping, which summarizes potentially complicated spillover effects into a fixed dimensional statistic of instrumental variables. We investigate identification conditions for the intention-to-treat effect and the average causal effect for compliers, while explicitly considering the possibility of misspecification of exposure mapping. Based on our identification results, we develop nonparametric estimation procedures via inverse probability weighting. Their asymptotic properties, including consistency and asymptotic normality, are investigated using an approximate neighborhood interference framework, which is convenient for dealing with unknown forms of spillovers between individuals. For an empirical illustration, we apply our method to experimental data on the anti-conflict intervention school program

Doubly Robust Uniform Confidence Bands for Group-Time Conditional Average Treatment Effects in Difference-in-Differences

We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant'Anna (2021). Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group-time conditional average treatment effect given the covariate. Focusing on the case of a continuous covariate, we propose a three-step estimation procedure based on nonparametric local polynomial regressions and parametric estimation methods. Using uniformly valid distributional approximation results for empirical processes and multiplier bootstrapping, we develop doubly robust inference methods to construct uniform confidence bands for the group-time conditional average treatment effect function. The accompanying R package didhetero allows for easy implementation of the proposed methods.Comment: R package: https://tkhdyanagi.github.io/didhetero

Estimating Marginal Treatment Effects under Unobserved Group Heterogeneity

This paper studies endogenous treatment effect models in which individuals are classified into unobserved groups based on heterogeneous treatment choice rules. Such heterogeneity may arise, for example, when multiple treatment eligibility criteria and different preference patterns exist. Using a finite mixture approach, we propose a marginal treatment effect (MTE) framework in which the treatment choice and outcome equations can be heterogeneous across groups. Under the availability of valid instrumental variables specific to each group, we show that the MTE for each group can be separately identified using the local instrumental variable method. Based on our identification result, we propose a two-step semiparametric procedure for estimating the group-wise MTE parameters. We first estimate the finite-mixture treatment choice model by a maximum likelihood method and then estimate the MTEs using a series approximation method. We prove that the proposed MTE estimator is consistent and asymptotically normally distributed. We illustrate the usefulness of the proposed method with an application to economic returns to college education

Systemic adverse events after screening of retinopathy of prematurity with mydriatic.

Purpose:To evaluate systemic adverse events after screening for retinopathy of prematurity (ROP) performed with mydriatic.Methods:This was a retrospective case series study. Medical records of consecutive patients who underwent screening for ROP with 0.5% phenylephrine and 0.5% tropicamide eyedrops were retrospectively reviewed. The score of abdominal distention (0-5), volume of milk sucked and volume of stool, along with systemic details (pulse and respiration rates, blood pressure and number of periods of apnea) were collected at 1 week and 1 day before ROP examination, and at 1 day after examination. Results were compared between the days before and after examination. Correlation between body weight at the time of examination and the score of abdominal distention was examined. The numbers of infants with abdominal and/or systemic adverse events were compared between pre- and post-examination periods.Results:Eighty-six infants met the inclusion criteria. The score of abdominal distention increased from 2.0 at 1 day before examination to 2.3 at 1 day after examination (p = 0.005), and the number of infants who had worsened abdominal distension increased after examination (p = 0.01). Infants with lower body weight had a higher score of abdominal distention (p < 0.0001, r = -0.57). The number of infants with reduced milk consumption increased after examination (p = 0.0001), as did the number of infants with decreased pulse rate (p = 0.0008).Conclusions:Screening for ROP with mydriatic may have adverse effects on systemic conditions. Infants should be carefully monitored after ROP screening with mydriatic

Effect of intravitreal bevacizumab for retinopathy of prematurity on weight gain.

Purpose:To evaluate the short-term effect on body weight (BW) gain after intravitreal bevacizumab (IVB) for retinopathy of prematurity (ROP).Methods:This was a retrospective 1:1 matched case-control study. Infants with ROP treated by IVB or photocoagulation (PC) at Shiga University of Medical Science Hospital between April 2010 and December 2019 were included in the study. To match BWs at treatment between the IVB and PC groups, 1:1 matching for BWs at treatment within 100 g was performed. The BW gains for the 7 days before treatment (pre-treatment week), the 7 days after treatment (first post-treatment week), and the period from 7 to 14 days after treatment (second post-treatment week) were compared between the IVB and PC groups.Results:Following 1:1 matching, 13 infants in both groups were enrolled in the analysis. The weekly BW gain for the first post-treatment week was significantly lower in the IVB group compared with the PC group (86 g vs. 145 g; P = 0.046), whereas the weekly BW gains for the pre-treatment week (173 g vs. 159 g; P = 0.71) and the second post-treatment week (154 g vs. 152 g; P = 0.73) were comparable between the two groups. The short-term inhibitive effect of IVB on BW gain was particularly observed in infants weighing less than 1500 g at treatment (<1500 g: 47 g vs. ≥1500 g: 132 g; P = 0.03).Conclusion:IVB could have a short-term inhibitive effect on BW gain in infants with ROP, and this effect is more likely to occur in infants with a lower BW at the time of treatment