Article type
Abstract
Background: The field of evidence-based policymaking has made considerable progress focusing on robust quantitative estimation of intervention effects. However, there is a growing recognition of the need to move beyond average treatment effects (ATE) and embrace a more nuanced understanding of equity and evidence considerations.
Objectives: This study addresses the imperative to advance equity and broaden evidence considerations by introducing Causal Forest (CF) analysis. The primary objective is to explore treatment effect heterogeneity within the context of a school-based gender attitude change program in Haryana, India. The emphasis is on moving beyond conventional metrics and investigating how various participant characteristics intersect with the program's outcomes.
Methods: The study employs CF analysis, a machine learning technique, along with a qualitative lens, to unravel treatment effect heterogeneity. This technique, developed by Stefan Wager and Susan Athey, uses an ensemble of decision trees (i.e., a random forest) to model heterogeneity and estimate a treatment effect, known as the Conditional ATE or CATE, for each study participant. CF analysis allows data to drive the identification of heterogenous sub-groups, reducing reliance on pre-specified characteristics. Furthermore, the integration of qualitative methods enriches outcome interpretation, providing policymakers with nuanced insights into contextual factors influencing program success.
Results: The results demonstrate the value of CF analysis paired with qualitative approaches in evidence generation. By exploring treatment effect heterogeneity, the study uncovers nuanced insights that traditional approaches, particularly those reliant on average effects, might overlook. Notably, the method sheds light on unexpected influences on outcomes, such as sibling age, challenging the limitations of conventional subgroup analyses and promoting a more equitable understanding of intervention effects.
Conclusions: In conclusion, this study proposes a novel method to advancing equity considerations in development interventions. By adopting CF analysis, there is a paradigm shift in evidence generation and decision-making processes. The method's potential, particularly in resource-constrained settings, is highlighted, allowing policymakers to target sub-groups effectively without the need for additional experiments. The study advocates for a more inclusive and informed approach to policymaking in the realm of development impact.
Objectives: This study addresses the imperative to advance equity and broaden evidence considerations by introducing Causal Forest (CF) analysis. The primary objective is to explore treatment effect heterogeneity within the context of a school-based gender attitude change program in Haryana, India. The emphasis is on moving beyond conventional metrics and investigating how various participant characteristics intersect with the program's outcomes.
Methods: The study employs CF analysis, a machine learning technique, along with a qualitative lens, to unravel treatment effect heterogeneity. This technique, developed by Stefan Wager and Susan Athey, uses an ensemble of decision trees (i.e., a random forest) to model heterogeneity and estimate a treatment effect, known as the Conditional ATE or CATE, for each study participant. CF analysis allows data to drive the identification of heterogenous sub-groups, reducing reliance on pre-specified characteristics. Furthermore, the integration of qualitative methods enriches outcome interpretation, providing policymakers with nuanced insights into contextual factors influencing program success.
Results: The results demonstrate the value of CF analysis paired with qualitative approaches in evidence generation. By exploring treatment effect heterogeneity, the study uncovers nuanced insights that traditional approaches, particularly those reliant on average effects, might overlook. Notably, the method sheds light on unexpected influences on outcomes, such as sibling age, challenging the limitations of conventional subgroup analyses and promoting a more equitable understanding of intervention effects.
Conclusions: In conclusion, this study proposes a novel method to advancing equity considerations in development interventions. By adopting CF analysis, there is a paradigm shift in evidence generation and decision-making processes. The method's potential, particularly in resource-constrained settings, is highlighted, allowing policymakers to target sub-groups effectively without the need for additional experiments. The study advocates for a more inclusive and informed approach to policymaking in the realm of development impact.