The Impact Modelling Tool: An AI-Enhanced Framework for Projecting Long-Term Socioeconomic Impact from Short-Term Evaluation Evidence
Webinar (em inglês) | Online
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Organizado por:
Department of Community Development (DCD)
Sobre o evento
Impact evaluations produce rigorous estimates of short-term programme effects, yet policymakers require long-term projections to inform strategic resource allocation, lifecycle planning, and accountability. This session presents the Impact Modelling Tool (IMT), an AI-enhanced simulation framework that bridges the translation gap between credible short-term causal estimates and the long-term socioeconomic trajectories that decision-makers need to design more responsive and inclusive policies.
The IMT operates through a six-layer conceptual architecture. It begins with empirically estimated causal anchors derived from rigorous impact evaluations (e.g., Randomised Controlled Trials, or Quasi-Experimental Designs such as Difference-in-Differences, Matching, and Regression Discontinuity Design) and propagates these effects across the life course using microsimulation, state-transition models, and agent-based approaches. A novel synaptic weight architecture decomposes model parameters into core weights fixed from causal evidence, context-specific adjustments calibrated by domain expertise, and AI-adapted weights learned within evidence-based bounds. This hybrid design preserves causal integrity whilst leveraging AI to capture heterogeneity, nonlinear interactions, and path dependencies across beneficiary subgroups and life stages.
The framework addresses four critical policy needs: 1) a lifecycle perspective on intervention returns; 2) accurate Value for Money calculations over extended time horizons; 3) policy coherence across interventions at different life stages; and 4) sustainability assessment of net benefits. The AI enhancement layer operates under strict governance constraints, including bound constraints, monotonicity requirements, and expert review, ensuring that projections remain transparent, interpretable, and defensible for public debate.
The session illustrates possible IMT’s application within the social sector in Abu Dhabi, where long-term impact projections inform interventions spanning youth employment, family support, and elderly care. It demonstrates how AI-enabled impact estimation can complement traditional evaluation, amplify the voices of communities through integrated survey and administrative data, and strengthen the evidence base for people-centred policymaking.
In brief, evaluation practice increasingly confronts the challenge of translating short-term findings into the long-term evidence that policymakers and the public require for informed decision-making. Without reliable projections, the full value of social interventions remains invisible, undermining public accountability and resource allocation. The IMT offers an original methodological response—combining established causal inference with governed AI enhancement—that is both rigorous and practically relevant to the evaluation community. In brief, this session aims to: (1) introduce the conceptual architecture and its causal-AI design principle as an innovation in evaluation methodology; (2) demonstrate how transparent, bounded AI adaptation can enrich impact projections without compromising rigour; and (3) illustrate the framework’s contribution to evidence-informed policymaking that centres the wellbeing of local communities and amplifies diverse perspectives in public policy.
The session will employ a structured 30-minute (plus 15-minute QA) format: the policy challenge of the evidence horizon gap and its implications for policy making (5 minutes); a walkthrough of the six-layer architecture with visual diagrams (10 minutes); a worked example tracing a causal anchor through the life stages (10 minutes); and reflections on governance, transparency, and implications for evaluation practice (5 minutes). To foster active audience engagement, the presentation will incorporate a live polling question on the acceptability and governance of AI-enhanced projections in participants’ evaluation contexts, with results displayed and briefly discussed during the closing reflections. As part of the final Q&A session (15 minutes) Audience members will be invited to share their own experiences of bridging short-term evidence and long-term policy needs.
The IMT operates through a six-layer conceptual architecture. It begins with empirically estimated causal anchors derived from rigorous impact evaluations (e.g., Randomised Controlled Trials, or Quasi-Experimental Designs such as Difference-in-Differences, Matching, and Regression Discontinuity Design) and propagates these effects across the life course using microsimulation, state-transition models, and agent-based approaches. A novel synaptic weight architecture decomposes model parameters into core weights fixed from causal evidence, context-specific adjustments calibrated by domain expertise, and AI-adapted weights learned within evidence-based bounds. This hybrid design preserves causal integrity whilst leveraging AI to capture heterogeneity, nonlinear interactions, and path dependencies across beneficiary subgroups and life stages.
The framework addresses four critical policy needs: 1) a lifecycle perspective on intervention returns; 2) accurate Value for Money calculations over extended time horizons; 3) policy coherence across interventions at different life stages; and 4) sustainability assessment of net benefits. The AI enhancement layer operates under strict governance constraints, including bound constraints, monotonicity requirements, and expert review, ensuring that projections remain transparent, interpretable, and defensible for public debate.
The session illustrates possible IMT’s application within the social sector in Abu Dhabi, where long-term impact projections inform interventions spanning youth employment, family support, and elderly care. It demonstrates how AI-enabled impact estimation can complement traditional evaluation, amplify the voices of communities through integrated survey and administrative data, and strengthen the evidence base for people-centred policymaking.
In brief, evaluation practice increasingly confronts the challenge of translating short-term findings into the long-term evidence that policymakers and the public require for informed decision-making. Without reliable projections, the full value of social interventions remains invisible, undermining public accountability and resource allocation. The IMT offers an original methodological response—combining established causal inference with governed AI enhancement—that is both rigorous and practically relevant to the evaluation community. In brief, this session aims to: (1) introduce the conceptual architecture and its causal-AI design principle as an innovation in evaluation methodology; (2) demonstrate how transparent, bounded AI adaptation can enrich impact projections without compromising rigour; and (3) illustrate the framework’s contribution to evidence-informed policymaking that centres the wellbeing of local communities and amplifies diverse perspectives in public policy.
The session will employ a structured 30-minute (plus 15-minute QA) format: the policy challenge of the evidence horizon gap and its implications for policy making (5 minutes); a walkthrough of the six-layer architecture with visual diagrams (10 minutes); a worked example tracing a causal anchor through the life stages (10 minutes); and reflections on governance, transparency, and implications for evaluation practice (5 minutes). To foster active audience engagement, the presentation will incorporate a live polling question on the acceptability and governance of AI-enhanced projections in participants’ evaluation contexts, with results displayed and briefly discussed during the closing reflections. As part of the final Q&A session (15 minutes) Audience members will be invited to share their own experiences of bridging short-term evidence and long-term policy needs.
Orador/a
| Nome | Título | Biography |
|---|---|---|
| Michele Binci | Dr | Dr Michele Binci is a development economist and impact evaluation specialist. Michele is currently a Social Impact Advisor at the Department of Community Development (DCD), in Abu Dhabi. He specialises in impact evaluation, causal inference, and AI-enhanced analytical frameworks for public policy. His work focuses on developing evaluation frameworks and methodologies that bridge rigorous evidence and strategic decision-making for social sector organisations. |