Assessing Pre-trial Services Using Machine-Learning Matching Algorithms - Info and Reading Options

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Shortly following arrest, judicial officers must decide whether to detain the arrested person in jail or to release him or her back into the community while awaiting trial. This is an extremely important decision in a criminal case (Bechtel, Holsinger, Lowenkamp, Warren, 2017). This decision relates to later case decisions (e.g., Ulmer, 2012; Kutateladze, Andiloro, Johnson, & Spohn, 2014), case outcomes (Oleson, Lowenkamp, Wooldredge, VanNostrand, & Cadigan, 2015), as well as outcomes even after a case is disposed (Cadigan & Lowenkamp, 2011; Lowenkamp, VanNostrand, & Holsinger, 2013). For example, those detained pre-trial are much more likely than those released pre-trial to plead guilty (Patterson & Lynch, 1991; Sutton, 2013), to be convicted of a felony (Schlesinger, 2007), and to receive a longer final sentence (Sacks & Ackerman, 2012). According to McCoy (2007), the decision to detain or release someone pre-trial is so critical that it determines mostly everything in a criminal case. Both the American Bar Association (2002) and the National Association of Pre-trial Services Agencies (2004) strongly recommend the use of an objective and research-based pre-trial risk assessment instrument to assist judicial officers’ in making this decision. One goal of these instruments is to identify people who are likely to recidivate. Researchers and practitioners have developed various pre-trial risk assessment instruments within the last two decades. Some prominent examples include the Virginia Pre-trial Risk Assessment Instrument (VPRAI) developed by the Virginia Department of Criminal Justice Services (VanNostrand, 2003), and the Public Safety Assessment (PSA) developed by the Laura and John Arnold Foundation (Lowenkamp, VanNostrand, & Holsinger, 2013; VanNostrand & Lowenkamp, 2013). Desmarais, Zottola, Clarke, and Lowder (2020) reviewed several risk assessment instruments including the VPRAI and PSA and found that they predicted recidivism with good to excellent accuracy. For example, the VPRAI discriminated those who had new arrests during the pre-trial period from those who did not 64 – 69% of the time. Moreover, these instruments were similarly predictive across racial and ethnic groups and were similarly predictive for both men and women. Still, Desmarais et al. emphasized the need for continued investigation of the predictive accuracy of pre-trial risk assessment instruments. Once judicial officers decide to release someone back into the community, they either release that person under supervision or without any supervision. Pre-trial supervision comes with certain conditions and restrictions that can include periodic check-ins with a case manager, maintaining employment, ankle monitoring, alcohol testing and treatment, and cognitive behavioral therapy (Clarke, 1988; Mamalian, 2011; VanNostrand & Keebler, 2009; VanNostrand, Rose, & Weibrecht, 2011). Those released pre-trial may also have access to social services that include opportunities to take part in education programs or employment training as well as transitional housing. The goal of pre-trial supervision is to provide an alternative to detention while minimizing recidivism and failures to appear in court. It is not clear whether pre-trial supervision reduces recidivism more so than simply releasing people pre-trial. The available research on the effectiveness of pre-trial supervision is limited. Bechtel et al. (2017) conducted a meta-analysis of 16 studies that investigated the impact of various pre-trial supervision conditions (e.g., ankle monitoring) on recidivism and found that none of the conditions reduced recidivism. Bechtel et al. tempered these findings by emphasizing that the quality of the research included in the meta analysis was poor and that the field of pre-trial research lacks methodological rigor. For example, they state, “the quality of the research that could be included in the current analysis was not very good” (p. 460). Elsewhere, they note that, “it is striking that although more than 800 potential studies on pre-trial were identified, less than 20% contained data, and the percentage of studies with the information necessary to synthesize the findings into a meta-analytic review was even lower than 20%” (p. 459). In fact, of the 16 studies that were included in the meta-analysis only four studies were peer-reviewed. They also added that there is a “great need for new and more rigorous pre-trial research in all related areas” (p. 459). They conclude by calling for researchers to “conduct methodologically rigorous studies that are submitted to peer-reviewed journals” (p. 463). Here, we answer the calls from Desmarais et al. (2020) and Bechtel et al. (2017). First, we compare the predictive accuracy of the VPRAI and the PSA. Second, we use two modern machine-learning-based matching algorithms to determine the causal impact of pre-trial supervision on recidivism. These algorithms, called Fast Large-scale Almost Matching Exactly -- FLAME (Wang et al. (2021) and Dynamic Almost Matching Exactly -- DAME (Liu, Dieng, Roy, Rudin, and Volfovsky (2019), stem from a new causal inference framework called Almost Matching Exactly, or Learning-to-Match. We discuss these machine-learning algorithms in detail below, but in general, they match people who receive pre-trial supervision to similar individuals who were released pre-trial. The virtue of this machine-learning matching process is that it establishes causality and can therefore test whether pre-trial supervision has a causal effect on recidivism. Next, we detail our confirmatory and exploratory hypotheses.

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