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Without controlling for selection bias and the potential endogeneity of the treatment by using proper methods, the estimation of treatment effect could lead to biased or incorrect conclusions. However, these issues are not addressed adequately and properly in higher education research. This study reviews the essence of self-selection bias, treatment assignment endogeneity, and treatment effect estimation. We introduce three treatment effect estimators – propensity score matching analysis, doubly robust estimation (augmented inverse probability weighted approach), and endogenous treatment estimator (control-function approach) – and examine literature that applies these methods to research in higher education. We then use the three methods in a case study that estimates the effects of transfer student pre-enrollment debt on persistence and first year grades. The final discussion provides guidelines and recommendations for causal inference research studies that use such quasi-experimental methods.

The purpose of this paper is to study the impact of unemployment on coronary heart diseases (CHD) in Italy on a sample of male manual workers in the private sector.

The authors investigate the association between CHD and different unemployment experiences (ever unemployed; short, mid and long cumulative unemployment), exploiting a large Italian administrative database on careers and health. The study design is based on the balancing of individuals' characteristics during a 12-year pre-treatment period; the measurement of unemployment occurrence during a seven-year treatment period; the observation of CHD occurrence during a five-year follow up. The workers characteristics and the probability of receiving the treatment are balanced by means of propensity score matching. Standard diagnostics on the balancing assumption are discussed and satisfied, while the robustness to violations of the unconfoundedness assumption is evaluated by a simulation-based sensitivity analysis.

The authors find a significant increase of CHD probability was found among workers who experience more than three years of unemployment (relative risks (RR)=1.91, p<0.1), and among those who exit unemployment starting a self-employment activity (RR=1.70, p<0.1). Using different selections of the study population, a clear pattern emerges: the healthier and more labour market attached are workers during pre-treatment, the greater is the negative impact of long-term unemployment on health (RR=2.79, p<0.01).

The very large representative sample (n=69,937) and the deep longitudinal dimension of the data (1985-2008) allowed the authors to minimize the risks of health selection and unemployment misclassification. Moreover, the adopted definition of unemployment corrected some undercoverage and misclassification issues that affect studies based on a purely administrative definition and that treat unemployment as a unique career event disregarding the duration of the experience.

The purpose of this paper is to illustrate how to examine the effectiveness of a pilot summer bridge program for elementary algebra using propensity scores. Typically, selection into treatment programs, such as summer bridge programs, is based on self-selection. Self-selection makes it very difficult to estimate the true treatment effect because the selection process itself often introduces a source of bias.

By using propensity scores, the authors can match students who participated in the summer bridge program with equivalent students who did not participate in the summer bridge program. By matching students in the treatment group to equivalent students who do not participate in the treatment, the authors can obtain an unbiased estimate of the treatment effect. The authors also describe a method to conduct a sensitivity analysis to estimate the amount of hidden bias generated from unobserved factors that would be needed to alter the inferences made from a propensity score matching analysis.

Findings suggest there is no significant difference in the pass rates of the subsequent intermediate algebra course for students who participated in the summer bridge program when compared to matched students who did not participate in the summer bridge program. Thus, students who participate in the summer bridge program fared no better or worse when compared to similar students who do not participate in the program. These findings also appear to be robust to hidden bias.

This study describes a unique way to estimate the causal effect of participating in a treatment program when there is self-selection into the treatment program.

Lechner and Miquel (2001) approached the causal analysis of sequences of interventions from a potential outcome perspective based on selection-on-observables-type assumptions (sequential conditional independence assumptions). Lechner (2004) proposed matching estimators for this framework. However, many practical issues that might have substantial consequences for the interpretation of the results have not been thoroughly investigated so far. This chapter discusses some of these practical issues. The discussion is related to estimates based on an artificial data set for which the true values of the parameters are known and that shares many features of data that could be used for an empirical dynamic matching analysis.

In the evaluation of most interventions in criminal justice settings, evaluators have no control over assignment to treatment and control/comparison conditions, which means that the treated and comparison groups may have differences that lead to biased conclusions regarding treatment effectiveness. Propensity score analysis can be used to balance the differences in the groups, which can be used in a number of ways to reduce biased conclusions regarding effectiveness. A review of propensity scoring studies was conducted for this chapter, where the limited number of evaluations of criminal justice interventions using these methods was identified. Due to the small number of these studies, research was also reviewed if propensity scoring had been employed to evaluate interventions that are similar to those in criminal justice systems. These studies are used as examples to demonstrate how the methods can be used to evaluate criminal justice interventions, the different ways propensity scores can be used to analyse treatment and comparison group differences, and the strengths and limitations of this approach. It is concluded that, while not appropriate for all interventions/settings, propensity score analysis can be useful in criminal justice arenas, at least to investigate the comparability of treatment and comparison groups, with suspected non-comparability being a common weakness of traditional quasi-experimental studies and frequently cited limitation in terms of drawing efficacy conclusions from such evaluations.

In healthcare, overuse and underuse of medical treatments represent equally dangerous deviations from an optimal use equilibrium and arouse concerns about possible implications for patients’ health, and for the healthcare system in terms of both costs and access to medical care. Medical liability plays a dominant role among the elements that can affect these deviations. Therefore, a remarkable economic literature studies how medical decisions are influenced by different levels of liability. In particular, identifying the relation between liability and treatments selection, as well as disentangling the effect of liability from other incentives that might be in place, is a task for sound empirical research. Several studies have already tried to tackle this issue, but much more needs to be done. In this chapter, we offer an overview of the state of the art in the study of the relation between liability and treatments selection. First, we reason on the theoretical mechanisms underpinning the relationship under investigation by presenting the main empirical predictions of the related literature. Second, we provide a comprehensive summary of the existing empirical evidence and its main weaknesses. Finally, we conclude by offering guidelines for further research.

The purpose of this paper is to describe an application of Multi-Criteria Decision Making (MCDM) technique for the selection of waste treatment and disposal technology for municipal solid waste (MSW).

The proposed approach is based on the integration of Delphi and Analytic Hierarchy Process (AHP) techniques. A model has been proposed to evaluate the best treatment and disposal technology. Expert opinions have been incorporated in the selection of criteria. AHP has been used to determine the weights of criteria, followed by ranking of the available technologies.

Delphi method was used to derive appropriate evaluation criteria to assess the potential alternative technologies. A set of identified holistic criteria was used, representing the environmental, social, and economic aspects, as compared to the sub-criteria concept generally found in existing literature. Quantitative weightings from the AHP model were calculated to identify the priorities of alternatives. The study provides a simple framework for technology selection as compared to the complex models present in the literature, reducing the uncertainty, cost and time consumed in the decision-making process.

The model identifies the optimal technologies for the handling, treatment and disposal of MSW in a better economic and more environmentally sustainable way. The study provides a simple framework for selection as compared to the complex models present in the literature, reducing the uncertainty, cost and time taken by the decision-making process.

The paper highlights a new insight into MCDM techniques to select an optimum treatment and disposal technology suitable for MSW management in India. The study identifies a minimal relevant set of evaluation criteria, and appropriate technologies for the handling, treatment, and disposal of MSW in a more economic and environmentally sustainable way.

Selecting the most appropriate healthcare waste treatment technology (HCWTT) is an uncertain and complex decision-making problem because there exist more than one alternative and many conflicting qualitative and quantitative criteria. However, the use of fuzzy and comparative values, instead of specific crisp values, provides more accurate results, so that the alternatives may be evaluated in accordance with hesitant human nature. The purpose of this paper is to select the best HCWTT using a hesitant fuzzy linguistic term set (HFLTS).

Five main criteria were identified for HCWTT selection, such as economic, social, environmental, technical and ergonomic criteria. In total, 19 sub-criteria were examined, and the hierarchy of the criteria was formed. The criteria weights were determined using the multi-criteria hesitant fuzzy linguistic term set (MC-HFLTS). The selection processes of incineration (A₁), steam sterilization (A₂), microwave (A₃) and landfill (A₄) alternatives were carried out using the multi-attributive ideal-real comparative analysis (MAIRCA) and multi-attributive border approximation area comparison (MABAC) methods. In the comparative analyses, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to an ideal solution (TOPSIS) methods were used.

The comparison of the results of the MABAC and MAIRCA methods with the results of VIKOR and TOPSIS methods indicated that A₂ (steam sterilization) alternative was the best one and produced the same ranking of the technology alternatives (A₂ > A₃ > A₁ > A₄). As a result, the study concluded that these methods can be successfully used for HCWTT selection problems.

To the best of the authors’ knowledge, MC-HFLTS has not been used to select HCWTT in the existing literature. For the first time, MC-HFLTS&MAIRCA and MC-HFLTS&MABAC approaches were used in order to choose the best treatment method for healthcare waste under the effect of multiple conflicting hierarchical criteria. It has been provided that MABAC and MAIRCA select alternative choices by taking into consideration the hierarchical criteria. Unlike other studies, this study also considered ergonomic criteria that are important for people working during the process of using the treatment technology.

Accreditation to the ISO 9001 Quality Management Systems Standard has proven to be a persistent and growing phenomenon in services and manufacturing, yet to date little attempt has been made to explore how performance results in cross‐sectional research may be attributed to different causation mechanisms and how their influences may alter over time. This paper aims to fill this gap.

The paper defines four possible causation mechanisms before searching and analysing the empirical literature on quality management system certification to ISO 9001 and business performance for evidence of their causal influence.

From the analyses, it is found that the benefit that can safely be attributed to the treatment‐effect of ISO 9001 accreditation is lower waste; while the benefits of lower costs and better quality are less likely unless motives for adoption are developmental rather than externally driven. From an analysis of longitudinal studies a strong selection‐mechanism is found where more profitable firms have a greater propensity to adopt than less profitable firms. From the finding propositions are developed to show how the influence of these mechanisms change over time.

The existence of the selection‐mechanism has profound implications for interpreting business performance achievements because the benefits that are attributed to the treatment‐effect from adopting quality management system standards are likely to be greatly inflated by the influence of the selection‐mechanism. The author suggests that richer theory is needed that can incorporate bi‐directional influences and new research is needed to explore the underlying causes of the selection effect.

The paper is believed to be the first to systematically explore attribution of performance in the ISO 9001 literature. Its findings provide new insights into the complexities of attribution of performance in studies of new practices and systems.

This chapter presents a Bayesian analysis of the endogenous treatment model with misclassified treatment participation. Our estimation procedure utilizes a combination of data augmentation, Gibbs sampling, and Metropolis–Hastings to obtain estimates of the misclassification probabilities and the treatment effect. Simulations demonstrate that the proposed Bayesian estimator accurately estimates the treatment effect in light of misclassification and endogeneity.