arXiv subjects

Hyunseung Kang

Publications and source records attributed to Hyunseung Kang.

36 records · Page 2Linked to original sources

arXiv2021

Assumption-Lean Analysis of Cluster Randomized Trials in Infectious Diseases for Intent-to-Treat Effects and Network Effects

Cluster randomized trials (CRTs) are a popular design to study the effect of interventions in infectious disease settings. However, standard analysis of CRTs primarily relies on strong parametric methods, usually mixed-effect models to account for the clustering structure, and focuses on the overall intent-to-treat (ITT) effect to evaluate effectiveness. The paper presents two assumption-lean methods to analyze two types of effects in CRTs, ITT effects and network effects among well-known compliance groups. For the ITT effects, we study the overall and the heterogeneous ITT effects among the observed covariates where we do not impose parametric models or asymptotic restrictions on cluster size. For the network effects among compliance groups, we propose a new bound-based method that uses pre-treatment covariates, classification algorithms, and a linear program to obtain sharp bounds. A key feature of our method is that the bounds can become narrower as the classification algorithm improves and the method may also be useful for studies of partial identification with instrumental variables. We conclude by reanalyzing a CRT studying the effect of face masks and hand sanitizers on transmission of 2008 interpandemic influenza in Hong Kong.

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arXiv2021

Weak-Instrument Robust Tests in Two-Sample Summary-Data Mendelian Randomization

Mendelian randomization (MR) has been a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome using genetic variants as instrumental variables (IV), with two-sample summary-data MR being the most popular. Unfortunately, instruments in MR studies are often weakly associated with the exposure, which can bias effect estimates and inflate Type I errors. In this work, we propose test statistics that are robust under weak instrument asymptotics by extending the Anderson-Rubin, Kleibergen, and the conditional likelihood ratio test in econometrics to two-sample summary-data MR. We also use the proposed Anderson-Rubin test to develop a point estimator and to detect invalid instruments. We conclude with a simulation and an empirical study and show that the proposed tests control size and have better power than existing methods with weak instruments.

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arXiv2021

Detecting Heterogeneous Treatment Effect with Instrumental Variables

There is an increasing interest in estimating heterogeneity in causal effects in randomized and observational studies. However, little research has been conducted to understand heterogeneity in an instrumental variables study. In this work, we present a method to estimate heterogeneous causal effects using an instrumental variable approach. The method has two parts. The first part uses subject-matter knowledge and interpretable machine learning techniques, such as classification and regression trees, to discover potential effect modifiers. The second part uses closed testing to test for the statistical significance of the effect modifiers while strongly controlling familywise error rate. We conducted this method on the Oregon Health Insurance Experiment, estimating the effect of Medicaid on the number of days an individual's health does not impede their usual activities, and found evidence of heterogeneity in older men who prefer English and don't self-identify as Asian and younger individuals who have at most a high school diploma or GED and prefer English.

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arXiv2020

Increasing Power for Observational Studies of Aberrant Response: An Adaptive Approach

In many observational studies, the interest is in the effect of treatment on bad, aberrant outcomes rather than the average outcome. For such settings, the traditional approach is to define a dichotomous outcome indicating aberration from a continuous score and use the Mantel-Haenszel test with matched data. For example, studies of determinants of poor child growth use the World Health Organization's definition of child stunting being height-for-age z-score $\leq -2$. The traditional approach may lose power because it discards potentially useful information about the severity of aberration. We develop an adaptive approach that makes use of this information and asymptotically dominates the traditional approach. We develop our approach in two parts. First, we develop an aberrant rank approach in matched observational studies and prove a novel design sensitivity formula enabling its asymptotic comparison with the Mantel-Haenszel test under various settings. Second, we develop a new, general adaptive approach, the two-stage programming method, and use it to adaptively combine the aberrant rank test and the Mantel-Haenszel test. We apply our approach to a study of the effect of teenage pregnancy on stunting.

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arXiv2020

Debiased Inverse-Variance Weighted Estimator in Two-Sample Summary-Data Mendelian Randomization

Mendelian randomization (MR) has become a popular approach to study the effect of a modifiable exposure on an outcome by using genetic variants as instrumental variables. A challenge in MR is that each genetic variant explains a relatively small proportion of variance in the exposure and there are many such variants, a setting known as many weak instruments. To this end, we provide a theoretical characterization of the statistical properties of two popular estimators in MR, the inverse-variance weighted (IVW) estimator and the IVW estimator with screened instruments using an independent selection dataset, under many weak instruments. We then propose a debiased IVW estimator, a simple modification of the IVW estimator, that is robust to many weak instruments and doesn't require screening. Additionally, we present two instrument selection methods to improve the efficiency of the new estimator when a selection dataset is available. An extension of the debiased IVW estimator to handle balanced horizontal pleiotropy is also discussed. We conclude by demonstrating our results in simulated and real datasets.

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arXiv2020

ivmodel: An R Package for Inference and Sensitivity Analysis of Instrumental Variables Models with One Endogenous Variable

We present a comprehensive R software ivmodel for analyzing instrumental variables with one endogenous variable. The package implements a general class of estimators called k- class estimators and two confidence intervals that are fully robust to weak instruments. The package also provides power formulas for various test statistics in instrumental variables. Finally, the package contains methods for sensitivity analysis to examine the sensitivity of the inference to instrumental variables assumptions. We demonstrate the software on the data set from Card (1995), looking at the causal effect of levels of education on log earnings where the instrument is proximity to a four-year college.

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arXiv2020

Two Robust Tools for Inference about Causal Effects with Invalid Instruments

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents two tools to conduct valid inference and tests in the presence of invalid instruments. First, we propose a simple and general approach to construct confidence intervals based on taking unions of well-known confidence intervals. Second, we propose a novel test for the null causal effect based on a collider bias. Our two proposals, especially when fused together, outperform traditional instrumental variable confidence intervals when invalid instruments are present, and can also be used as a sensitivity analysis when there is concern that instrumental variables assumptions are violated. The new approach is applied to a Mendelian randomization study on the causal effect of low-density lipoprotein on the incidence of cardiovascular diseases.

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arXiv2020

Inferring Treatment Effects After Testing Instrument Strength in Linear Models

A common practice in IV studies is to check for instrument strength, i.e. its association to the treatment, with an F-test from regression. If the F-statistic is above some threshold, usually 10, the instrument is deemed to satisfy one of the three core IV assumptions and used to test for the treatment effect. However, in many cases, the inference on the treatment effect does not take into account the strength test done a priori. In this paper, we show that not accounting for this pretest can severely distort the distribution of the test statistic and propose a method to correct this distortion, producing valid inference. A key insight in our method is to frame the F-test as a randomized convex optimization problem and to leverage recent methods in selective inference. We prove that our method provides conditional and marginal Type I error control. We also extend our method to weak instrument settings. We conclude with a reanalysis of studies concerning the effect of education on earning where we show that not accounting for pre-testing can dramatically alter the original conclusion about education's effects.

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arXiv2019

Inference After Selecting Plausibly Valid Instruments with Application to Mendelian Randomization

Mendelian randomization (MR) is a popular method in genetic epidemiology to estimate the effect of an exposure on an outcome by using genetic instruments. These instruments are often selected from a combination of prior knowledge from genome wide association studies (GWAS) and data-driven instrument selection procedures or tests. Unfortunately, when testing for the exposure effect, the instrument selection process done a priori is not accounted for. This paper studies and highlights the bias resulting from not accounting for the instrument selection process by focusing on a recent data-driven instrument selection procedure, sisVIVE, as an example. We introduce a conditional inference approach that conditions on the instrument selection done a priori and leverage recent advances in selective inference to derive conditional null distributions of popular test statistics for the exposure effect in MR. The null distributions can be characterized with individual-level or summary-level data in MR. We show that our conditional confidence intervals derived from conditional null distributions attain the desired nominal level while typical confidence intervals computed in MR do not. We conclude by reanalyzing the effect of BMI on diastolic blood pressure using summary-level data from the UKBiobank that accounts for instrument selection.

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arXiv2019

Estimation Methods for Cluster Randomized Trials with Noncompliance: A Study of A Biometric Smartcard Payment System in India

Many policy evaluations occur in settings where treatment is randomized at the cluster level, and there is treatment noncompliance within each cluster. For example, villages might be assigned to treatment and control, but residents in each village may choose to comply or not with their assigned treatment status. When noncompliance is present, the instrumental variables framework can be used to identify and estimate causal effects. While a large literature exists on instrumental variables estimation methods, relatively little work has been focused on settings with clustered treatments. Here, we review extant methods for instrumental variable estimation in clustered designs and derive both the finite and asymptotic properties of these estimators. We prove that the properties of current estimators depend on unrealistic assumptions. We then develop a new IV estimation method for cluster randomized trials and study its formal properties. We prove that our IV estimator allows for possible treatment effect heterogeneity that is correlated with cluster size and is robust to low compliance rates within clusters. We evaluate these methods using simulations and apply them to data from a randomized intervention in India.

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arXiv2019

Spillover Effects in Cluster Randomized Trials with Noncompliance

Cluster randomized trials (CRTs) are popular in public health and in the social sciences to evaluate a new treatment or policy where the new policy is randomly allocated to clusters of units rather than individual units. CRTs often feature both noncompliance, when individuals within a cluster are not exposed to the intervention, and individuals within a cluster may influence each other through treatment spillovers where those who comply with the new policy may affect the outcomes of those who do not. Here, we study the identification of causal effects in CRTs when both noncompliance and treatment spillovers are present. We prove that the standard analysis of CRT data with noncompliance using instrumental variables does not identify the usual complier average causal effect when treatment spillovers are present. We extend this result and show that no analysis of CRT data can unbiasedly estimate local network causal effects. Finally, we develop bounds for these causal effects under the assumption that the treatment is not harmful compared to the control. We demonstrate these results with an empirical study of a deworming intervention in Kenya.

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arXiv2018

Testing Endogeneity with High Dimensional Covariates

Modern, high dimensional data has renewed investigation on instrumental variables (IV) analysis, primarily focusing on estimation of effects of endogenous variables and putting little attention towards specification tests. This paper studies in high dimensions the Durbin-Wu-Hausman (DWH) test, a popular specification test for endogeneity in IV regression. We show, surprisingly, that the DWH test maintains its size in high dimensions, but at an expense of power. We propose a new test that remedies this issue and has better power than the DWH test. Simulation studies reveal that our test achieves near-oracle performance to detect endogeneity.

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arXiv2018

Inference for Instrumental Variables: A Randomization Inference Approach

The method of instrumental variables (IV) provides a framework to study causal effects in both randomized experiments with noncompliance and in observational studies where natural circumstances produce as-if random nudges to accept treatment. Traditionally, inference for IV relied on asymptotic approximations of the distribution of the Wald estimator or two-stage least squares, often with structural modeling assumptions and/or moment conditions. In this paper, we utilize the randomization inference approach to IV inference. First, we outline the exact method, which uses the randomized assignment of treatment in experiments as a basis for inference, but lacks a closed-form solution and may be computationally infeasible in many applications. We then provide an alternative to the exact method, the almost exact method, which is computationally feasible but retains the advantages of the exact method. We also review asymptotic methods of inference, including those associated with two-stage least squares, and analytically compare them to randomization inference methods. We also perform additional comparisons using a set of simulations. We conclude with three different applications from the social sciences.

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arXiv2017

Confidence Intervals for Causal Effects with Invalid Instruments using Two-Stage Hard Thresholding with Voting

A major challenge in instrumental variables (IV) analysis is to find instruments that are valid, or have no direct effect on the outcome and are ignorable. Typically one is unsure whether all of the putative IVs are in fact valid. We propose a general inference procedure in the presence of invalid IVs, called Two-Stage Hard Thresholding (TSHT) with voting. TSHT uses two hard thresholding steps to select strong instruments and generate candidate sets of valid IVs. Voting takes the candidate sets and uses majority and plurality rules to determine the true set of valid IVs. In low dimensions, if the sufficient and necessary identification condition under invalid instruments is met, which is more general than the so-called 50% rule or the majority rule, our proposal (i) correctly selects valid IVs, (ii) consistently estimates the causal effect, (iii) produces valid confidence intervals for the causal effect, and (iv) has oracle-optimal width. In high dimensions, we establish nearly identical results without oracle-optimality. In simulations, our proposal outperforms traditional and recent methods in the invalid IV literature. We also apply our method to re-analyze the causal effect of education on earnings.

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arXiv2016

Peer Encouragement Designs in Causal Inference with Partial Interference and Identification of Local Average Network Effects

In non-network settings, encouragement designs have been widely used to analyze causal effects of a treatment, policy, or intervention on an outcome of interest when randomizing the treatment was considered impractical or when compliance to treatment cannot be perfectly enforced. Unfortunately, such questions related to treatment compliance have received less attention in network settings and the most well-studied experimental design in networks, the two-stage randomization design, requires perfect compliance with treatment. The paper proposes a new experimental design called peer encouragement design to study network treatment effects when enforcing treatment randomization is not feasible. The key idea in peer encouragement design is the idea of personalized encouragement, which allows point-identification of familiar estimands in the encouragement design literature. The paper also defines new causal estimands, local average network effects, that can be identified under the new design and analyzes the effect of non-compliance behavior in randomized experiments on networks.

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arXiv2016

A simple and robust confidence interval for causal effects with possibly invalid instruments

Instrumental variables have been widely used to estimate the causal effect of a treatment on an outcome. Existing confidence intervals for causal effects based on instrumental variables assume that all of the putative instrumental variables are valid; a valid instrumental variable is a variable that affects the outcome only by affecting the treatment and is not related to unmeasured confounders. However, in practice, some of the putative instrumental variables are likely to be invalid. This paper presents a simple and general approach to construct a confidence interval that is robust to possibly invalid instruments. The robust confidence interval has theoretical guarantees on having the correct coverage and can also be used to assess the sensitivity of inference when instrumental variables assumptions are violated. The paper also shows that the robust confidence interval outperforms traditional confidence intervals popular in instrumental variables literature when invalid instruments are present. The new approach is applied to a developmental economics study of the causal effect of income on food expenditures.

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arXiv2015

Full Matching Approach to Instrumental Variables Estimation with Application to the Effect of Malaria on Stunting

Most previous studies of the causal relationship between malaria and stunting have been studies where potential confounders are controlled via regression-based methods, but these studies may have been biased by unobserved confounders. Instrumental variables (IV) regression offers a way to control for unmeasured confounders where, in our case, the sickle cell trait can be used as an instrument. However, for the instrument to be valid, it may still be important to account for measured confounders. The most commonly used instrumental variable regression method, two-stage least squares, relies on parametric assumptions on the effects of measured confounders to account for them. Additionally, two-stage least squares lacks transparency with respect to covariate balance and weighing of subjects and does not blind the researcher to the outcome data. To address these drawbacks, we propose an alternative method for IV estimation based on full matching. We evaluate our new procedure on simulated data and real data concerning the causal effect of malaria on stunting among children. We estimate that the risk of stunting among children with the sickle cell trait decrease by 0.22 times the average number of malaria episodes prevented by the sickle cell trait, a substantial effect of malaria on stunting (p-value: 0.011, 95% CI: 0.044, 1).

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arXiv2014

Instrumental Variables Estimation with Some Invalid Instruments and its Application to Mendelian Randomization

Instrumental variables have been widely used for estimating the causal effect between exposure and outcome. Conventional estimation methods require complete knowledge about all the instruments' validity; a valid instrument must not have a direct effect on the outcome and not be related to unmeasured confounders. Often, this is impractical as highlighted by Mendelian randomization studies where genetic markers are used as instruments and complete knowledge about instruments' validity is equivalent to complete knowledge about the involved genes' functions. In this paper, we propose a method for estimation of causal effects when this complete knowledge is absent. It is shown that causal effects are identified and can be estimated as long as less than $50$% of instruments are invalid, without knowing which of the instruments are invalid. We also introduce conditions for identification when the 50% threshold is violated. A fast penalized $\ell_1$ estimation method, called sisVIVE, is introduced for estimating the causal effect without knowing which instruments are valid, with theoretical guarantees on its performance. The proposed method is demonstrated on simulated data and a real Mendelian randomization study concerning the effect of body mass index on health-related quality of life index. An R package \emph{sisVIVE} is available online.

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