Characterization of Overlap in Observational Studies

Overlap between treatment groups is required for non-parametric estimation of causal effects. If a subgroup of subjects always receives the same intervention, we cannot estimate the effect of intervention changes on that subgroup without further assumptions. When overlap does not hold globally, characterizing local regions of overlap can inform the relevance of causal conclusions for new subjects, and can help guide additional data collection. To have impact, these descriptions must be interpretable for downstream users who are not machine learning experts, such as policy makers. We formalize overlap estimation as a problem of finding minimum volume sets subject to coverage constraints and reduce this problem to binary classification with Boolean rule classifiers. We then generalize this method to estimate overlap in off-policy policy evaluation. In several real-world applications, we demonstrate that these rules have comparable accuracy to black-box estimators and provide intuitive and informative explanations that can inform policy making.Comment: To appear at AISTATS 202

Discrete Optimization for Interpretable Study Populations and Randomization Inference in an Observational Study of Severe Sepsis Mortality

Motivated by an observational study of the effect of hospital ward versus intensive care unit admission on severe sepsis mortality, we develop methods to address two common problems in observational studies: (1) when there is a lack of covariate overlap between the treated and control groups, how to define an interpretable study population wherein inference can be conducted without extrapolating with respect to important variables; and (2) how to use randomization inference to form confidence intervals for the average treatment effect with binary outcomes. Our solution to problem (1) incorporates existing suggestions in the literature while yielding a study population that is easily understood in terms of the covariates themselves, and can be solved using an efficient branch-and-bound algorithm. We address problem (2) by solving a linear integer program to utilize the worst case variance of the average treatment effect among values for unobserved potential outcomes that are compatible with the null hypothesis. Our analysis finds no evidence for a difference between the sixty day mortality rates if all individuals were admitted to the ICU and if all patients were admitted to the hospital ward among less severely ill patients and among patients with cryptic septic shock. We implement our methodology in R, providing scripts in the supplementary material

Gait analysis in a <i>Mecp2</i> knockout mouse model of Rett syndrome reveals early-onset and progressive motor deficits

Rett syndrome (RTT) is a genetic disorder characterized by a range of features including cognitive impairment, gait abnormalities and a reduction in purposeful hand skills. Mice harbouring knockout mutations in the &lt;i&gt;Mecp2&lt;/i&gt; gene display many RTT-like characteristics and are central to efforts to find novel therapies for the disorder. As hand stereotypies and gait abnormalities constitute major diagnostic criteria in RTT, it is clear that motor and gait-related phenotypes will be of importance in assessing preclinical therapeutic outcomes. We therefore aimed to assess gait properties over the prodromal phase in a functional knockout mouse model of RTT. In male &lt;i&gt;Mecp2&lt;/i&gt; knockout mice, we observed alterations in stride, coordination and balance parameters at 4 weeks of age, before the onset of other overt phenotypic changes as revealed by observational scoring. These data suggest that gait measures may be used as a robust and early marker of &lt;i&gt;Mecp2&lt;/i&gt;-dysfunction in future preclinical therapeutic studies