Selection of invalid instruments can improve estimation in Mendelian randomization

Mendelian randomization (MR) is a widely-used method to identify causal links between a risk factor and disease. A fundamental part of any MR analysis is to choose appropriate genetic variants as instrumental variables. Current practice usually involves selecting only those genetic variants that are deemed to satisfy certain exclusion restrictions, in a bid to remove bias from unobserved confounding. Many more genetic variants may violate these exclusion restrictions due to unknown pleiotropic effects (i.e. direct effects on the outcome not via the exposure), but their inclusion could increase the precision of causal effect estimates at the cost of allowing some bias. We explore how to optimally tackle this bias-variance trade-off by carefully choosing from many weak and locally invalid instruments. Specifically, we study a focused instrument selection approach for publicly available two-sample summary data on genetic associations, whereby genetic variants are selected on the basis of how they impact the asymptotic mean square error of causal effect estimates. We show how different restrictions on the nature of pleiotropic effects have important implications for the quality of post-selection inferences. In particular, a focused selection approach under systematic pleiotropy allows for consistent model selection, but in practice can be susceptible to winner's curse biases. Whereas a more general form of idiosyncratic pleiotropy allows only conservative model selection, but offers uniformly valid confidence intervals. We propose a novel method to tighten honest confidence intervals through support restrictions on pleiotropy. We apply our results to several real data examples which suggest that the optimal selection of instruments does not only involve biologically-justified valid instruments, but additionally hundreds of potentially pleiotropic variants.Comment: 56 pages, 8 figure

Models of random wildlife movement with an application to distance sampling

In this paper we present three models of random wildlife movement: a one dimensional model of wildlife-observer encounters on roads, an analogous two dimensional model, and an further two-dimensional model that borrows from the ideas of statistical mechanics. We then derive unbiased estimates of wildlife density in terms of encounters for each of these models. By extending these results to incorporate uncertain detection, we suggest three novel distance sampling methods and briefly consider possible field applications

Hidden hazards and screening policy : predicting undetected lead exposure in Illinois

Lead exposure still threatens children’s health despite policies aiming to identify lead exposure sources. Some US states require de jure universal screening while others target screening, but little research examines the relative benefits of these approaches. We link lead tests for children born in Illinois between 2010 and 2014 to geocoded birth records and potential exposure sources. We train a random forest regression model that predicts children’s blood lead levels (BLLs) to estimate the geographic distribution of undetected lead poisoning. We use these estimates to compare de jure universal screening against targeted screening. Because no policy achieves perfect compliance, we analyze different incremental screening expansions. We estimate that 5,819 untested children had a BLL≥ 5µ/dL, in addition to the 18,101 detected cases. 80% of these undetected cases should have been screened under the current policy. Model-based targeted screening can improve upon both the status quo and expanded universal screening

Hidden hazards and screening policy: predicting undetected lead exposure in Illinois

Lead exposure still threatens children’s health despite policies aiming to identify lead exposure sources. Some US states require de jure universal screening while others target screening, but little research examines the relative benefits of these approaches. We link lead tests for children born in Illinois between 2010 and 2014 to geocoded birth records and potential exposure sources. We train a random forest regression model that predicts children’s blood lead levels (BLLs) to estimate the geographic distribution of undetected lead poisoning. We use these estimates to compare de jure universal screening against targeted screening. Because no policy achieves perfect compliance, we analyze different incremental screening expansions. We estimate that 5,819 untested children had a BLL≥ 5µ/dL, in addition to the 18,101 detected cases. 80% of these undetected cases should have been screened under the current policy. Model-based targeted screening can improve upon both the status quo and expanded universal screening

Yes, Wall Street, There Is a January Effect! Evidence from Laboratory Auctions

In the first experimental test of the January effect, we find an economically large and statistically significant result in two very different auction environments. After controlling for variables that could influence subjectsÕ bids such as differences in private values, cumulative earnings, and learning effects, the prices in the January markets were systematically higher than those in December. The results suggest that psychological factors may contribute to the well-documented January effect in empirical stock market data, a conclusion that clearly violates the efficient markets hypothesis.