Tailoring health policies to improve outcomes using machine learning, causal inference and operations research methods

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To maximise the impact of health policies on population health and improve the equitable distribution of health, policymakers require answers to questions such as: does the policy work for the intended recipients? Who benefits most? Does the policy reduce health inequalities? Who should be eligible for a programme? To generate evidence to answer these questions, policy evaluations need to go beyond the average population impact and consider how impacts differ across different types of individuals (treatment effect heterogeneity across subgroups). While such subgroup analysis has been done before, the previously used approaches are limited in that they open the door to the researcher cherry-picking the subgroups on the basis of what turns out as statistically significant in the estimates. By contrast, machine learning techniques - automated algorithms that learn from the data - can reveal patterns in the policy impact that may not be expected beforehand. This is important for policymakers who need to understand who benefits most (and who does less, or not at all) from the implemented policy in question. Once we are able to assess how a given policy affects different sub-groups of the population and by how much, we can take these insights and design the eligibility criteria of a health policies, so that they maximise a decision maker's objective, for example by generating the most total health benefit given a fixed health care budget. The combination of machine learning with methods that can estimate causal impacts of policy is relatively recent area of research, in particular their application to learn about "treatment effect heterogeneity" and the targeting of policies. Hence, there is no methodological guidance available on how to apply these recent tools in health policy evaluations. The proposed research aims to make a contribution by assessing and extending the available approaches to address the specific challenges that typically arise when evaluating health policies. These include statistical challenges, such as the need to account for potential biases due to observed and unobserved differences between the treated and control groups; but also challenges to make evaluations relevant to decision making, by considering not just the benefits but also the costs of an intervention (cost effectiveness), and also considering budget constraints or considerations of equity when designing which population subgroups should be targeted with a policy.This project proposes to address these challenges, by assessing and extending recently proposed machine learning and causal inference methods in the context of health policy evaluations and also by combining tools from different disciplines: causal inference, machine learning and cost-effectiveness modelling, for the first time. By successfully addressing these challenges, this project will deliver methods that will help researchers and policymakers carry-out more comprehensive evaluations of country-wide health policies. This could help support significant improvements to population health and reduce the health gap between the rich and poor within countries. The methodological developments are motivated by two case studies from a low- and middle-income country context, where the gains in terms of improving health and reducing health inequalities are particularly large. The case studies focus on two large scale health policies with ongoing relevance: major public health insurance reform in Indonesia and the country-wide Family Health Programme in Brazil.To maximise impact on current health policy making, design of the specific research questions in the case studies will benefit from on-going input from Indonesian and Brazilian collaborators as well as policymakers. With extensive communication and impact activities, this project will make its methodological insights available for researchers working on health policy evaluations, in academia and beyond.