For too long, risk predictive modeling has represented the core of machine learning analytics, leaving real-world evidence (RWE) of treatment effectiveness untouched. As many have found out, predictions alone do not lead to student success outcomes, often being used to discourage students. Further, their opaque and nonlinear nature can lead to human suspicions and more often an exercise of explaining scores instead of taking actions. Randomized controlled trials (RCTs) are slow, expensive, and sometimes unethical. Furthermore, population heterogeneities can make such RCT results difficult to replicate. That is, treatments need to be personalized to various student segments and measuring a single treatment across all student groups can dull the true effects of interventions designed for heterogeneous student populations, including students who are likely to experience equity gaps.
In the face of the pandemic, higher-ed institutions are facing unprecedented challenges from enrollment to mental wellbeing of students and staff to persistence to post-graduation employment success. Institutions are implementing a number of policies and investing in technologies and vendor solutions without being able to measure the true effectiveness of these solutions.
CML Insight was founded to help institutions go beyond predictive analytics and to democratize causal machine learning (ML). Causal ML is the science of finding causal relationships between interventions and multiple outcomes that matter to institutions using retrospective data. The discovery of causal insights in heterogeneous populations is the key ingredient in building intentional prospective interventions that can help students in a prospective, multi-armed bandit experimental design. In this sense, causal ML is the bridge that connects RWE to prospective experiments that test highly-promising interventions discovered through RWE.
