Hyunseung Kang

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 article, 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 ℓ1 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(BMI) on health-related quality of life (HRQL) index. An R package sisVIVE is available on CRAN. Supplementary materials for this article are available online.

The causal effect of malaria on stunting: a Mendelian randomization and matching approach

BACKGROUND Previous studies on the association of malaria and stunted growth delivered inconsistent results. These conflicting results may be due to different levels of confounding and to considerable difficulties in elucidating a causal relationship. Randomized experiments are impractical and previous observational studies have not fully controlled for potential confounding including nutritional deficiencies, breastfeeding habits, other infectious diseases and socioeconomic status. METHODS This study aims to estimate the causal effect between malaria episodes and stunted growth by applying a combination of Mendelian randomization, using the sickle cell trait, and matching. We demonstrate the method on a cohort of children in the Ashanti Region, Ghana. RESULTS We found that the risk of stunting increases by 0.32 (P-value: 0.004, 95% CI: 0.09, 1.0) for every malaria episode. The risk estimate based on Mendelian randomization substantially differs from the multiple regression estimate of 0.02 (P-value: 0.02, 95% CI: 0.003, 0.03). In addition, based on the sensitivity analysis, our results were reasonably insensitive to unmeasured confounders. CONCLUSIONS The method applied in this study indicates a causal relationship between malaria and stunting in young children in an area of high endemicity and demonstrates the usefulness of the sickle cell trait as an instrument for the analysis of conditions that might be causally related to malaria.

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).

Commentary: Matched Instrumental Variables

Matching in ObservatiOnal studies and shi et al. matching is a popular technique to deduce causal effects of a treatment on an outcome in observational data. In brief, matching individuals in groups with different values of the treatment (for cohort designs), but similar values of the observed covariates, so that within each group, the only difference between the individuals is their treatment values; for case–control designs, matching is done on the outcome, instead of on the treatment. then, under the usual set of causal identifying assumptions (conditional ignorability, consistency, and positivity), one can estimate the average causal effect of a treatment on an outcome. While there are many other techniques to estimate causal effects in observational data, including standardization, g-formula, g-estimation, inverse probability weighting, stratification, and targeted maximum likelihood estimation (see references 5–10 for textbook discussions), matching has some advantages over these methods. First, matching is transparent in assessing covariate balance. that is, if there are values of covariates for which almost all individuals have a high (or low) value of the treatment, then matching and its associated diagnostics will tell us that matched sets cannot be formed. Second, matching is blind to the outcome data; a matching algorithm only requires the measured covariates and the treatment values. more importantly, matching diagnostics and covariate balance checks can be done all without looking at the outcome data. Finally, for estimation, matching is nonparametric; it does not use any parametric modeling assumptions, such as linearity. For more discussions and recent overviews, see. In reference 1, the authors used matching to analyze the causal effect of trauma care from different trauma centers (treatment) on emergency department mortality (outcome). Because there were three treatment arms/trauma centers, level I and II trauma centers and nontrauma centers, the authors employed triplet matching where each patient from nontrauma centers were matched exactly with one patient from level I and II trauma centers, forming a triplet. Ideally, the patients in a matched triplet were similar with respect to the eight covariates about patient demographics and health.

Genome-wide association study reveals sex-specific genetic architecture of facial attractiveness

Facial attractiveness is a complex human trait of great interest in both academia and industry. Literature on sociological and phenotypic factors associated with facial attractiveness is rich, but its genetic basis is poorly understood. In this paper, we conducted a genome-wide association study to discover genetic variants associated with facial attractiveness using 3,928 samples in the Wisconsin Longitudinal Study. We identified two genome-wide significant loci and highlighted a handful of candidate genes, many of which are specifically expressed in human tissues involved in reproduction and hormone synthesis. Additionally, facial attractiveness showed strong and negative genetic correlations with BMI in females and with blood lipids in males. Our analysis also suggested sex-specific selection pressure on variants associated with lower male attractiveness. These results revealed sex-specific genetic architecture of facial attractiveness and provided fundamental new insights into its genetic basis.

Confidence intervals for causal effects with invalid instruments by 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.