Hypothesis Testing, “Dutch Book” Arguments, and Risk

“Dutch Book” arguments and references to gambling theorems are typical in the debate between Bayesians and scientists committed to “classical” statistical methods. These arguments have rarely convinced non-Bayesian scientists to abandon certain conventional practices (like fixed-level null hypothesis significance testing), partially because many scientists feel that gambling theorems have little relevance to their research activities. In other words, scientists “don’t bet.” This article examines one attempt, by Schervish, Seidenfeld, and Kadane, to progress beyond such apparent stalemates by connecting “Dutch Book”–type mathematical results with principles actually endorsed by practicing experimentalists.</jats:p

Explaining the Behavior of Black-Box Prediction Algorithms with Causal Learning

We propose to explain the behavior of black-box prediction methods (e.g., deep neural networks trained on image pixel data) using causal graphical models. Specifically, we explore learning the structure of a causal graph where the nodes represent prediction outcomes along with a set of macro-level "interpretable" features, while allowing for arbitrary unmeasured confounding among these variables. The resulting graph may indicate which of the interpretable features, if any, are possible causes of the prediction outcome and which may be merely associated with prediction outcomes due to confounding. The approach is motivated by a counterfactual theory of causal explanation wherein good explanations point to factors which are "difference-makers" in an interventionist sense. The resulting analysis may be useful in algorithm auditing and evaluation, by identifying features which make a causal difference to the algorithm's output

Causal Inference With Outcome-Dependent Missingness And Self-Censoring

We consider missingness in the context of causal inference when the outcome of interest may be missing. If the outcome directly affects its own missingness status, i.e., it is "self-censoring", this may lead to severely biased causal effect estimates. Miao et al. [2015] proposed the shadow variable method to correct for bias due to self-censoring; however, verifying the required model assumptions can be difficult. Here, we propose a test based on a randomized incentive variable offered to encourage reporting of the outcome that can be used to verify identification assumptions that are sufficient to correct for both self-censoring and confounding bias. Concretely, the test confirms whether a given set of pre-treatment covariates is sufficient to block all backdoor paths between the treatment and outcome as well as all paths between the treatment and missingness indicator after conditioning on the outcome. We show that under these conditions, the causal effect is identified by using the treatment as a shadow variable, and it leads to an intuitive inverse probability weighting estimator that uses a product of the treatment and response weights. We evaluate the efficacy of our test and downstream estimator via simulations.Comment: 15 pages. In proceedings of the 39th Conference on Uncertainty in Artificial Intelligenc

The Effect of the Earned Income Tax Credit on Physical and Mental health—Results from the Atlanta Paycheck Plus Experiment

Policy Points: The Paycheck Plus randomized controlled trial tested a fourfold increase in the Earned Income Tax Credit (EITC) for single adults without dependent children r over 3 years in New York and Atlanta. In New York, the intervention improved economic, mental, and physical health outcomes. In Atlanta, it had no economic benefit or impact on physical health r and may have worsened mental health. In Atlanta, tax filing and bonus receipt were lower than in the New York arm of the trial, which may explain the lack of economic benefits. Lower mental health scores in the treatment group were driven by disadvantaged men, and the study sample was in good mental health

Optical Fiber LSPR Biosensor Prepared by Gold Nanoparticle Assembly on Polyelectrolyte Multilayer

This article provides a novel method of constructing an optical fiber localized surface plasmon resonance (LSPR) biosensor. A gold nanoparticle (NP) assembled film as the sensing layer was built on the polyelectrolyte (PE) multilayer modified sidewall of an unclad optical fiber. By using a trilayer PE structure, we obtained a monodisperse gold NP assembled film. The preparation procedure for this LSPR sensor is simple and time saving. The optical fiber LSPR sensor has higher sensitivity and outstanding reproducibility. The higher anti-interference ability for response to an antibody makes it a promising method in application as a portable immuno-sensor