A partial dependence factorial analysis to deal with selection bias in observational studis

This thesis presents a creative and practical approach to dealing with the problem of selection bias. Selection bias may be the most important vexing problem in program evaluation or in any line of research that attempts to assert causality. Some of the greatest minds in economics and statistics have scrutinized the problem of selection bias, with the resulting approaches – Rubin’s Potential Outcome Approach(Rosenbaum and Rubin,1983; Rubin, 1991,2001,2004) or Heckman’s Selection model (Heckman, 1979) – being widely accepted and used as the best fixes. These solutions to the bias that arises in particular from self selection are imperfect, and many researchers, when feasible, reserve their strongest causal inference for data from experimental rather than observational studies. The innovative aspect of this thesis is to propose a data transformation that allows measuring and testing in an automatic and multivariate way the presence of selection bias. The approach involves the construction of a multi-dimensional conditional space of the X matrix in which the bias associated with the treatment assignment has been eliminated. Specifically, we propose the use of a partial dependence analysis of the X-space as a tool for investigating the dependence relationship between a set of observable pre-treatment categorical covariates X and a treatment indicator variable T, in order to obtain a measure of bias according to their dependence structure. The measure of selection bias is then expressed in terms of inertia due to the dependence between X and T that has been eliminated. Given the measure of selection bias, we propose a multivariate test of imbalance in order to check if the detected bias is significant, by using the asymptotical distribution of inertia due to T (Estadella et al. 2005) , and by preserving the multivariate nature of data. Further, we propose the use of a clustering procedure as a tool to find groups of comparable units on which estimate local causal effects, and the use of the multivariate test of imbalance as a stopping rule in choosing the best cluster solution set. The method is non parametric, it does not call for modeling the data, based on some underlying theory or assumption about the selection process, but instead it calls for using the existing variability within the data and letting the data to speak. The idea of proposing this multivariate approach to measure selection bias and test balance comes from the consideration that in applied research all aspects of multivariate balance, not represented in the univariate variable- by-variable summaries, are ignored. The first part contains an introduction to evaluation methods as part of public and private decision process and a review of the literature of evaluation methods. The attention is focused on Rubin Potential Outcome Approach, matching methods, and briefly on Heckman’s Selection Model. The second part focuses on some resulting limitations of conventional methods, with particular attention to the problem of how testing in the correct way balancing. The third part contains the original contribution proposed , a simulation study that allows to check the performance of the method for a given dependence setting and an application to a real data set. Finally, we discuss, conclude and explain our future perspectives.

Analysis of technological accidents triggered by natural events (Natech) in the perspective of climate change

The severe accidents deriving from the impact of natural events on industrial installations have become a matter of growing concern in the last decades. In the literature, these events are typically referred to as Natech accidents. Several peculiarities distinguish them from conventional industrial accidents caused by internal factors, such as the possible occurrence of multiple simultaneous failures, and the enhanced probability of cascading events. The research project provides a comprehensive overview of Natech accidents that occurred in the Chemical and Process Industry, allowing for the identification of relevant aspects of Natech events. Quantified event trees and probability of ignition are derived from the collected dataset, providing a step forward in the quantitative risk assessment of Natech accidents. The investigation of past Natech accidents also demonstrated that wildfires may cause technological accidents. Climate change and global warming are promoting the conditions for wildfire development and rapid spread. Hence, ensuring the safety of industrial facilities exposed to wildfires is paramount. This was achieved defining safety distances between wildland vegetation and industrial equipment items. In addition, an innovative methodology for the vulnerability assessment of Natech and Domino scenarios triggered by wildfires was developed. The approach accounted for the dynamic behaviour of wildfire events and related technological scenarios. Besides, the performance of the emergency response and the related intervention time in the case of cascading events caused by natural events were evaluated. Overall, the tools presented in this thesis represent a step forward in the Quantitative Risk Assessment of Natech accidents. The methodologies developed also provide a solid basis for the definition of effective strategies for risk mitigation and reduction. These aspects are crucial to improve the resilience of industrial plants to natural hazards, especially considering the effects that climate change may have on the severity of such events.