Causal Analysis at Extreme Quantiles with Application to London Traffic Flow Data

Transport engineers employ various interventions to enhance traffic-network performance. Recent emphasises on cycling as a sustainable travel mode aims to reduce traffic congestion. Quantifying the impacts of Cycle Superhighways is complicated due to the non-random assignment of such an intervention over the transport network and heavy-tailed distribution of traffic flow. Treatment effects on asymmetric and the heavy-tailed distributions are better reflected at extreme tails rather than at averages or intermediate quantiles. In such situations, standard methods for estimating quantile treatment effects at the extremes can provide misleading inference due to the high variability of estimates. In this work, we propose a novel method to estimate the treatment effect at extreme tails incorporating heavy-tailed feature in the outcome distribution. Simulation results show the superiority of the proposed method over existing estimators for quantile causal effects at extremes. The analysis of London transport data utilising the proposed method indicates that the traffic flow increased substantially after the Cycle Superhighway came into operation. The findings can assist government agencies in effective decision making to avoid high consequence events and improve network performance.Comment: arXiv admin note: text overlap with arXiv:2003.0899

Inference for a Class of Partially Observed Point Process Models

This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process (PP's) models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo (SMC) methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applied in the context of finance

Inference for a Class of Partially Observed Point Process Models

This paper presents a simulation-based framework for sequential inference from partially and discretely observed point process (PP's) models with static parameters. Taking on a Bayesian perspective for the static parameters, we build upon sequential Monte Carlo (SMC) methods, investigating the problems of performing sequential filtering and smoothing in complex examples, where current methods often fail. We consider various approaches for approximating posterior distributions using SMC. Our approaches, with some theoretical discussion are illustrated on a doubly stochastic point process applied in the context of finance

An Evaluation Framework for Personalization Strategy Experiment Designs

Online Controlled Experiments (OCEs) are the gold standard in evaluating the effectiveness of changes to websites. An important type of OCE evaluates different personalization strategies, which present challenges in low test power and lack of full control in group assignment. We argue that getting the right experiment setup -- the allocation of users to treatment/analysis groups -- should take precedence of post-hoc variance reduction techniques in order to enable the scaling of the number of experiments. We present an evaluation framework that, along with a few simple rule of thumbs, allow experimenters to quickly compare which experiment setup will lead to the highest probability of detecting a treatment effect under their particular circumstance.Comment: Presented in the AdKDD 2020 workshop, in conjunction with The 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, KDD 2020. Main paper: 7 pages, 2 figures, 2 tables, Supplementary document: 6 page

The mucosal firewalls against commensal intestinal microbes

Mammals coexist with an extremely dense microbiota in the lower intestine. Despite the constant challenge of small numbers of microbes penetrating the intestinal surface epithelium, it is very unusual for these organisms to cause disease. In this review article, we present the different mucosal firewalls that contain and allow mutualism with the intestinal microbiot

Evaluation of low traffic neighbourhood (LTN) impacts on NO2 and traffic

Traffic restriction measures may create safer and healthier places for community members but may also displace traffic and air pollution to surrounding streets. Effective urban planning depends on understanding the magnitude of changes resulting from policy measures, both within and surrounding intervention areas; these are largely unstudied in the case of Low traffic Neighbourhoods (LTN). We evaluated impacts of three LTNs in the London Borough of Islington, UK, on air pollution and traffic flows in and around intervention areas, based on monthly Nitrogen Dioxide (NO2) and traffic volume data provided by the local authority. We identified pre- and post-intervention monitoring periods and intervention, boundary and control sites. We then adapted the generalised difference in differences approach to evaluate the effects within LTNs and at their boundary. We found that LTNs have the potential to substantially reduce air pollution and traffic in target areas, without increasing air pollution or traffic volumes in surrounding streets. These results provide sound arguments in favour of LTNs to promote health and wellbeing in urban communities

Bayesian Doubly Robust Causal Inference via Loss Functions

Frequentist inference has a well-established supporting theory for doubly robust causal inference based on the potential outcomes framework, which is realized via outcome regression (OR) and propensity score (PS) models. The Bayesian counterpart, however, is not obvious as the PS model loses its balancing property in joint modeling. In this paper, we propose a natural and formal Bayesian solution by bridging loss-type Bayesian inference with a utility function derived from the notion of a pseudo-population via the change of measure. Consistency of the posterior distribution is shown with correctly specified and misspecified OR models. Simulation studies suggest that our proposed method can estimate the true causal effect more efficiently and achieve the frequentist coverage if either the OR model is correctly specified or fit with a flexible function of the confounders, compared to the previous Bayesian approach via the Bayesian bootstrap. Finally, we apply this novel Bayesian method to assess the impact of speed cameras on the reduction of car collisions in England