Evaluating the Effects of Standardized Patient Care Pathways on Clinical Outcomes

The main focus of this study is to create a standardized approach to evaluating the impact of the patient care pathways across all major disease categories and key outcome measures in a hospital setting when randomized clinical trials are not feasible. Toward this goal I identify statistical methods, control factors, and adjustments that can correct for potential confounding in observational studies. I investigate the efficiency of existing bias correction methods under varying conditions of imbalanced samples through a Monte Carlo simulation. The simulation results are then utilized in a case study for one of the largest primary diagnosis areas, chronic obstructive pulmonary disease (COPD) at the University of Tennessee Medical Center. The analysis of the COPD pathway effects on the readmission rates showed a significant positive impact, with reduction in the probability of readmissions between 12% and 16%. The reduction in the length of stay was reported across all the models with historical controls, but the effect was not statistically significant

Evaluating the Impact of Health Programmes

This paper has two broad objectives. The first objective is broadly methodological and deals with some of the more pertinent estimation issues one should be aware of when studying the impact of health status on economic outcomes. We discuss some alternatives for constructing counterfactuals when designing health program evaluations such as randomization, matching and instrumental variables. Our second objective is to present a review of the existing evidence on the impact of health interventions on individual welfare.

Manipulationism, Ceteris Paribus Laws, and the Bugbear of Background Knowledge

According to manipulationist accounts of causal explanation, to explain an event is to show how it could be changed by intervening on its cause. The relevant change must be a ‘serious possibility’ claims Woodward 2003, distinct from mere logical or physical possibility—approximating something I call ‘scientific possibility’. This idea creates significant difficulties: background knowledge is necessary for judgments of possibili-ty. Yet the primary vehicles of explanation in manipulationism are ‘invariant’ generali-sations, and these are not well adapted to encoding such knowledge, especially in the social sciences, as some of it is non-causal. Ceteris paribus (CP) laws or generalisa-tions labour under no such difficulty. A survey of research methods such as case and comparative studies, randomised control trials, ethnography, and structural equation modeling, suggests that it would be more difficult and in some instances impossible to try to represent the output of each method in invariant generalisations; and that this is because in each method causal and non-causal background knowledge mesh in a way that cannot easily be accounted for in manipulationist terms. Ceteris paribus-generalisations being superior in this regard, a theory of explanation based on the latter is a better fit for social science

Approximate and Situated Causality in Deep Learning

Altres ajuts: ICREA Academia 2019, and "AppPhil: Applied Philosophy for the Value-Design of Social Networks Apps" project, funded by Caixabank in Recercaixa2017.Causality is the most important topic in the history of western science, and since the beginning of the statistical paradigm, its meaning has been reconceptualized many times. Causality entered into the realm of multi-causal and statistical scenarios some centuries ago. Despite widespread critics, today deep learning and machine learning advances are not weakening causality but are creating a new way of finding correlations between indirect factors. This process makes it possible for us to talk about approximate causality, as well as about a situated causality

Learning Optimal Prescriptive Trees from Observational Data

We consider the problem of learning an optimal prescriptive tree (i.e., an interpretable treatment assignment policy in the form of a binary tree) of moderate depth, from observational data. This problem arises in numerous socially important domains such as public health and personalized medicine, where interpretable and data-driven interventions are sought based on data gathered in deployment -- through passive collection of data -- rather than from randomized trials. We propose a method for learning optimal prescriptive trees using mixed-integer optimization (MIO) technology. We show that under mild conditions our method is asymptotically exact in the sense that it converges to an optimal out-of-sample treatment assignment policy as the number of historical data samples tends to infinity. Contrary to existing literature, our approach: 1) does not require data to be randomized, 2) does not impose stringent assumptions on the learned trees, and 3) has the ability to model domain specific constraints. Through extensive computational experiments, we demonstrate that our asymptotic guarantees translate to significant performance improvements in finite samples, as well as showcase our uniquely flexible modeling power by incorporating budget and fairness constraints

Three essays on the economics of energy efficiency and conservation

In this dissertation, I analyze investment decisions and consumer behavior related to energy efficiency and conservation. This research is motivated by evidence that the benefits from energy savings can extend well beyond private monetary gains to consumers. By providing insight on these topics, I contribute to a broad literature on environmental economics. This dissertation is constituted of three chapters as follows. For the first chapter, I introduce a methodological contribution for statistical evaluation of the impact of policy changes, interventions, or general "treatments." Specifically, I focus on estimating treatment effect heterogeneity in event studies with staggered adoption: panel data settings where observational units experience treatment at different points in time. I propose using highly flexible machine learning algorithms to predict counterfactuals (unobserved outcomes in absence of treatment) in those settings. With simulations, I show that my proposed method can recover nuanced effects with more accuracy and with better statistical efficiency than traditional econometric models (such as fixed effects regressions). I conclude that chapter with an application of the ML approach to real data from the Weatherization Assistance Program (WAP), which is one of the largest residential energy efficiency programs in the US. I identify how energy savings differ substantially depending on housing structure and on which types of upgrades were performed. For example, I document how complete furnace replacements are associated with significantly higher savings than furnace repairs. Also, I show that measures related to wall, attic, and foundation insulation are among the strongest contributors to energy savings. Finally, I assess measure-specific cost-effectiveness. Those results are informative for efforts to improve the allocation of program funds. The second chapter of this dissertation consists of a randomized control trial (RCT) to assess if behavioral nudges can promote energy conservation in absence of direct monetary incentives. The RCT was conducted in a campus residence hall with students that payed a fixed fee for energy at the beginning of each term. In that setting, behavioral factors such as social norms and moral suasion are the main incentives for conservation, since the marginal monetary costs/savings for energy are zero. The RCT consisted of sending "home energy reports" to randomly selected students, revealing their heating/cooling energy consumption. The energy usage of their neighbors was also displayed. Results from analyzing high-frequency thermostat data suggest that those reports were not effective for changing students’ consumption patterns during the regular semester. On the other hand, nudges sent prior to school breaks resulted in significantly lower thermostats (thus lower energy consumption). That second finding suggests that the null effects during the regular semester are unlikely to be driven by students’ inattention or by lack of understanding on how to operate thermostats. Rather, students were more willing to lower setpoints before leaving for breaks, given that they would not be facing associated thermal discomfort in that case. Collectively, those results suggest that behavioral nudges alone may not be sufficient to promote energy conservation in settings were monetary incentives are lacking. For the last chapter of this dissertation, I investigate how and why rented dwellings are less likely to have energy efficient appliances. I use data from a representative sample of residences across the continental US. I document that, on average, dwellings are more efficient when landlords are responsible for paying energy bills, and in states with high energy prices. Those results are consistent with a well-documented problem of split incentives in residential markets: the "landlord-tenant problem." When tenants pay utility bills, landlords may have little incentive to invest in efficiency, especially if those investments do not translate into higher rents. That could happen due to information asymmetries that limit prospective tenants' abilities to fully compare rental units across the market. By analyzing the effects of tenancy duration on the adoption of efficient technologies, I find evidence that investments in owner-occupied homes are more likely to occur closer to move-in dates. On the other hand, investments in rented homes are more likely to occur at later periods of tenancy, when relations between landlords and tenants might be better established. Those results are suggestive of a sorting process in residential markets, where homeowners have a stronger preference for energy-efficient dwellings and may have smaller discount rates. Findings from this chapter reinforce that energy efficiency policies in rental markets should differ from those targeted at homeowners