Policy Impact Modeling

🔍 Evaluating Policy Impacts with Synthetic Control Methods: Recent Advances and Tools How do we know if the policies we create have the effects we desire? What if a policy is enacted in only one place (like a state) but not others? Enter the Synthetic Control Method (SCM) which offers a useful framework for these kinds of problems, especially when randomized experiments are infeasible (which is most of the time). 📘 Foundations of Synthetic Controls Introduced by Abadie and Gardeazabal (2003) and further developed by Abadie, Diamond, and Hainmueller (2010), SCM constructs a weighted combination of control units to approximate the counterfactual of a treated unit. If you are not familiar with SCM, a great place to start is Scott Cunningham's excellent book Causal Inference: The Mixtape: https://lnkd.in/g7YMb7KT. If you're interested in more advanced methods, keep reading. 1. Augmented Synthetic Control Method (ASCM): Combines SCM with regression adjustments to improve estimation accuracy. Reference: Ben-Michael, Feller, & Rothstein (2021). 2. Generalized Synthetic Control (GSC): Extends SCM to accommodate multiple treated units and time-varying effects. Reference: Xu (2017). 3. Synthetic Diff in Diff (synthdid): Accounts for staggered adoption or variable treatment times. Reference: Arkhangelsky et al. (2021). 🛠️ R Packages for Implementation Synth: https://lnkd.in/gQAc4NU3 augsynth: https://lnkd.in/gqzqs6EV gsynth: https://lnkd.in/gmAzuS3k synthdid: https://lnkd.in/gWVkRUhT tidysynth: https://lnkd.in/gSScV-ya 📚 Further Reading Abadie, A., Diamond, A., & Hainmueller, J. (2010). Synthetic Control Methods for Comparative Case Studies: Estimating the Effect of California’s Tobacco Control Program. Journal of the American Statistical Association, 105(490), 493–505. https://lnkd.in/g_jwdfri Arkhangelsky, D., et al. (2021). Synthetic Difference-in-Differences. American Economic Journal: Applied Economics, 13(2), 1–35. https://lnkd.in/gnWhCX4y Ben-Michael, E., Feller, A., & Rothstein, J. (2021). The Augmented Synthetic Control Method. Journal of the American Statistical Association, 116(536), 1789–1803. https://lnkd.in/gfSNPYih Xu, Y. (2017). Generalized Synthetic Control Method: Causal Inference with Interactive Fixed Effects Models. Political Analysis, 25(1), 57–76. Link Hazlett, C., & Xu, Y. (2018). Trajectory Balancing: A General Reweighting Approach to Estimating Treatment Effects in Synthetic Control Designs.  https://lnkd.in/g9d4_895 #CausalInference #SyntheticControl #PolicyEvaluation #RStats

🌍 New paper out in Nature Climate Change on a critical question for climate policy: How does policy sequencing impact energy decarbonization? Led by Huilin Luo and Wei Peng, with Allen Fawcett, Jessica Green, Gokul Iyer, Jonas Nahm and David G. Victor Our team used advanced energy modeling to examine "carrots" (subsidies like those in the Inflation Reduction Act) vs. "sticks" (carbon pricing) - and crucially, the ORDER in which they're deployed. Key findings: ✅ Carrots alone don't achieve deep decarbonization – sticks are needed ✅ Near-term impacts of carrots vary widely by sector and consistency ✅ Timing is critical: delaying carbon pricing by 20 years (vs. 10) increases the eventual price needed by 40% ✅ Carrots boost green industries but don't significantly phase out fossil fuels - sticks are essential for that ✅ With rapid innovation, carrots followed quickly by sticks can be nearly as cost-effective as leading with carbon pricing The research bridges political science and energy modeling to analyze real-world policy tradeoffs. While carbon taxes are economically "first-best," political reality often requires starting with industrial policy - making the transition strategy crucial. Check out Mark Purdon’s great commentary on the paper: Green Industrial Policy Is Not Enough for Deep Decarbonization https://lnkd.in/gURqCbUE Read the full paper: https://lnkd.in/gW52_bcC #ClimatePolicy #EnergyTransition #ClimateScience #InflationReductionAct #Decarbonization

Causal Analysis: Impact Evaluation and Causal Machine Learning with Applications in R   The book Causal Analysis by Martin Huber is a cutting‑edge resource that combines econometrics, causal inference, and machine learning with practical applications in R. It provides a rigorous yet accessible framework for evaluating interventions, policies, and treatments, making it essential for researchers, data scientists, and economists.   📘 Why This Book Matters Correlation is not causation. In modern data science and policy evaluation, understanding causal relationships is critical for making valid conclusions. This book equips readers with both classical econometric tools and modern causal machine learning techniques, ensuring robust and credible impact evaluations.   📑 Key Content Covered Foundations of Causality: Distinguishing correlation from causal effects. Social Experiments & Regression: Classical approaches to causal inference. Selection on Observables: Controlling for confounding variables. Causal Machine Learning: Leveraging algorithms for causal discovery. Instrumental Variables: Addressing endogeneity in models. Difference‑in‑Differences & Synthetic Controls: Evaluating policy interventions. Regression Discontinuity & Kink Designs: Identifying causal thresholds. Partial Identification & Sensitivity Analysis: Handling uncertainty in causal estimates. Treatment Evaluation under Interference: Advanced methods for complex systems.   💡 Key Benefits Comprehensive Toolkit: Covers econometrics, causal inference, and machine learning. Hands‑On R Applications: Practical coding examples for real datasets. Policy Relevance: Tools for evaluating interventions in economics, healthcare, and social sciences. Modern Perspective: Integrates classical methods with cutting‑edge ML approaches.   👥 Who Should Read It Economists & Policy Analysts: To evaluate interventions with rigor. Data Scientists & Statisticians: To apply causal ML in applied research. Researchers in Social & Health Sciences: To strengthen causal inference in studies. Graduate Students: To build expertise in econometrics and causal analysis.   🌍 The Professional Edge This book is more than a statistics manual—it is a strategic guide to understanding cause‑and‑effect in complex systems. By mastering its methods, professionals can move beyond correlations to uncover true causal relationships, driving smarter decisions in policy, economics, and data science. 🔖 Hashtags #CausalInference #MachineLearning #Econometrics #ImpactEvaluation #RProgramming #DataScience #PolicyAnalysis #ProfessionalDevelopment

𝗙𝗼𝗿 𝟮𝟬 𝘆𝗲𝗮𝗿𝘀, 𝘄𝗲 𝗽𝗿𝗲𝗽𝗮𝗿𝗲𝗱 𝗞𝗣𝗜𝘀. Now, this is only half of the job done. Erasmus+ Youth applications 𝗶𝗻 𝟮𝟬𝟮𝟲 𝘄𝗶𝗹𝗹 𝗹𝗼𝗼𝗸 𝘃𝗲𝗿𝘆 𝗱𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝘁. The policy roadmap is already here. If you want to predict the future of your project, 𝘆𝗼𝘂 𝗻𝗲𝗲𝗱 𝘁𝗼 𝗿𝗲𝗮𝗱 𝘁𝗵𝗲 𝗳𝗶𝗻𝗲 𝗽𝗿𝗶𝗻𝘁 of today's policy frameworks. It tells. 𝗙𝗼𝗿𝗴𝗲𝘁 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗞𝗣𝗜𝘀! The shift from "𝗰𝗼𝘂𝗻𝘁𝗶𝗻𝗴 𝗵𝗲𝗮𝗱𝘀" to "p𝗿𝗼𝘃𝗶𝗻𝗴 𝘀𝘆𝘀𝘁𝗲𝗺𝗶𝗰 𝗰𝗵𝗮𝗻𝗴𝗲" is explicitly documented in three key resources: 𝟭 𝗘𝗨 𝗬𝗼𝘂𝘁𝗵 𝗦𝘁𝗿𝗮𝘁𝗲𝗴𝘆 (𝟮𝟬𝟭𝟵–𝟮𝟬𝟮𝟳): Explicitly demands evidence-based policy making and "participatory evaluation" methods. 𝟮 𝗥𝗔𝗬 𝗡𝗲𝘁𝘄𝗼𝗿𝗸 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 (𝗥𝗔𝗬-𝗠𝗢𝗡/𝗟𝗧𝗘): The EU’s data backbone now prioritises verified impact over simple satisfaction scores. 𝟯 𝗖𝗼𝘂𝗻𝗰𝗶𝗹 𝗥𝗲𝘀𝗼𝗹𝘂𝘁𝗶𝗼𝗻 𝗼𝗻 𝗬𝗼𝘂𝘁𝗵 (𝟮𝟬𝟮𝟮–𝟮𝟬𝟮𝟳): Calls for systemic activity evaluation that links local project results to European policy goals. 𝗪𝗵𝗮𝘁 𝗰𝗵𝗮𝗻𝗴𝗲𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲𝘀𝗲 𝗯𝗿𝗶𝗻𝗴𝗶𝗻𝗴? We are seeing a move away from purely quantitative KPIs. The "tick-box" era of evaluation is ending. The Commission is signalling a 𝗻𝗲𝗲𝗱 𝗳𝗼𝗿 𝗻𝗮𝗿𝗿𝗮𝘁𝗶𝘃𝗲-𝗯𝗮𝘀𝗲𝗱 𝗶𝗺𝗽𝗮𝗰𝘁 𝗿𝗲𝗽𝗼𝗿𝘁𝗶𝗻𝗴—they want to know the "story" of the change, not just the number of participants. Reread it! We will need to 𝘁𝗲𝗹𝗹 𝘁𝗵𝗲 𝘀𝘁𝗼𝗿𝘆 𝗼𝗳 𝘄𝗵𝗮𝘁 𝗵𝗮𝗽𝗽𝗲𝗻𝗲𝗱, not the number of people! 𝗪𝗵𝗮𝘁 𝗱𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗺𝗲𝗮𝗻 𝗶𝗻 𝗽𝗿𝗮𝗰𝘁𝗶𝗰𝗲? To align with this shift for 2026, we need to upgrade our evaluation toolkits now. A hybrid model is emerging as the new gold standard: • 𝗠𝗼𝘀𝘁 𝗦𝗶𝗴𝗻𝗶𝗳𝗶𝗰𝗮𝗻𝘁 𝗖𝗵𝗮𝗻𝗴𝗲 (𝗠𝗦𝗖) 𝗠𝗼𝗱𝗲𝗹: To capture the qualitative, human stories of empowerment. • 𝗢𝘂𝘁𝗰𝗼𝗺𝗲 𝗛𝗮𝗿𝘃𝗲𝘀𝘁𝗶𝗻𝗴: To map those specific stories directly to the EU Youth Goals. • If you can prove the l͟i͟n͟k͟ ͟b͟e͟t͟w͟e͟e͟n͟ ͟a͟ ͟p͟a͟r͟t͟i͟c͟i͟p͟a͟n͟t͟'͟s͟ ͟p͟e͟r͟s͟o͟n͟a͟l͟ ͟s͟t͟o͟r͟y͟ ͟a͟n͟d͟ ͟a͟ ͟m͟a͟j͟o͟r͟ ͟E͟U͟ ͟p͟o͟l͟i͟c͟y͟ ͟o͟b͟j͟e͟c͟t͟i͟v͟e͟,͟ your proposal becomes incredibly difficult to reject. Are you preparing your evaluation frameworks for this shift?

What really happens when people lose Medicaid coverage? Medicaid recipients are high utilizers of emergency department services, (often at 4 to 5x their private insurance counterparts) but this phenomenon is due to extremely limited access to primary and preventive services. So Medicaid patients use their only rational option when ill: visit the emergency department where care is available and thorough. And when Medicaid coverage is indiscriminately ripped away? ED visits may drop—but not because people are healthier. Uninsured patients delay care until the illness is severe. They arrive sicker, often require admission, and are discharged sooner due to cost pressure—leading to worse outcomes, more readmissions, and higher mortality. Hospitals are hit on two fronts: 1. Fewer visits? Yes—but from deferred care, not reduced need. 2. Less funding? Absolutely. Medicaid reimburses. The uninsured often cannot. A projected loss of Medicaid for 13 million Americans could erase over $2.4B in hospital emergency department revenue revenue. In California alone, 1.3M losing Medi-Cal means 870,000 fewer ED visits—and $244M lost. But we can intervene. AI models—Random Forests, XGBoost—can predict which patients are likely to lose coverage. -Recommender systems can suggest personalized outreach. -Knowledge graphs uncover hidden risk from housing, employment, or transportation issues. -Digital twins simulate how policy changes impact real people. -Federated learning protects privacy while training smarter models. From this, we can: -Automate re-enrollment alerts -Match at-risk patients with navigators -Target mobile outreach to high-risk ZIP codes -Use feedback loops to fix barriers upstream So if coverage loss is truly unavoidable, we have to develop risk mitigation strategies now. And AI can give us a map. https://lnkd.in/gYA3y7ix

How do we estimate climate change macroeconomic risks in The World Bank's Country Climate and Development Reports? We just published a methodological paper that present a methodology used in many of them, with our partners at Industrial Economics (IEc). The methodology captures a set of impact channels through which climate change affects the economy by (1) connecting a set of biophysical models to the macroeconomic model and (2) exploring a set of development and climate scenarios. The paper summarizes the results for five countries, highlighting the sources and magnitudes of their vulnerability - with estimated gross domestic product losses in 2050 exceeding 10 percent of gross domestic product in some countries and scenarios, although only a small set of impact channels is included. The paper also presents estimates of the macroeconomic gains from sector-level adaptation interventions, considering their upfront costs and avoided climate impacts and finding significant net gross domestic product gains from adaptation opportunities identified in the Country Climate and Development Reports. Finally, the paper discusses the limits of current modeling approaches, and their complementarity with empirical approaches based on historical data series. I think there are strong complementarity between empirical approaches (which measure historical aggregated impacts and are key for calibration and validation) and process-based modeling (which can consider possible thresholds in the future and run policy counterfactuals). The paper is here: https://lnkd.in/gpAURDV5. Comments welcome! Kodzovi ABALO, Ph.D, Brent Boehlert, Thanh Bui (Tania), Andrew Burns, Unnada Chewpreecha, Charl Jooste, Florent McIsaac, Kim Smet, Kenneth Strzepek, and Diego Castillo and Heather Ruberl.

🎆 Excited to share that our paper, “Modeling the impacts of policy sequencing on energy decarbonization,” is out today in Nature Climate Change 🎉 Many countries have embraced climate policy strategies that emphasize large subsidies to deploy green technologies (‘carrots’) with the anticipation that more punitive policies (‘sticks’) may follow. But what does this sequencing mean for long-term decarbonization? Using the US as a case study, we explore different policy pathways—carrots only, sticks only, and various carrot-then-stick approaches—to understand how policy sequencing influences energy decarbonization. Main takeaways: - Carrots help with near-term mitigation, but sticks remain essential for long-term deep decarbonization - A carrots-first strategy works best when it speeds up innovation and is quickly followed by credible sticks - Policy durability matters: inconsistent carrots make decarbonization more costly and slower This work is part of our broader effort to bring political economy considerations into energy system modeling. I’m deeply grateful to our political science colleagues who have shaped my thinking—David G. Victor, Jonas Meckling Jonas Nahm Jessica Green—and to my fellow modelers—Gokul Iyer Allen Fawcett—for embracing the challenge of bringing politics into energy models. Special shoutout to Huilin Luo for publishing her first lead-author paper. Thanks also to Alfred P. Sloan Foundation for supporting this effort! - View-only full text: https://rdcu.be/eVPwH - Paper link from the journal: https://lnkd.in/eZ3x7Xvb

Modeling the world isn’t the same as making decisions in it! A common trap in applied AI, OR, and digital transformation is to spend months building a perfect simulator… a beautiful digital twin with clean architecture, smooth animations, and every physical nuance modeled. And yet… when it comes time to actually make decisions? No policy. No framework. Just dashboards and “what-if” buttons. Here’s the core mistake: We confuse modeling the system with modeling the decisions. A simulator helps you observe behavior. A policy helps you choose actions. They serve different purposes. At Toyota North America, I always separate the two: 🔹 Modeling the system (the physics, flows, stochastic processes) gives you a sandbox to play in. 🔹 Designing the policy means deciding how you’ll act over time, based on what you observe in the system. Want to optimize shuttle routing at a port? Great. Simulate vehicle movements, fueling stations, labor shifts, arrival patterns. But then design a policy that says when the shuttle leaves, who it picks up, and how it adjusts when demand surges. 🚫 A simulator is not a decision model. ✅ A simulator is the environment. Your policy is the intelligence. Throughout my career I’ve had entire projects spin in circles because nobody took the time to define: • What’s the decision? • When is it made? • Based on what information? • Using what logic? These four questions can help drive policy design and be the difference between pretty analytics and real ROI. Build your digital twin if you need to. But don’t forget to teach it how to act!

𝗡𝗲𝘄 𝗼𝗽𝗲𝗻-𝗮𝗰𝗰𝗲𝘀𝘀 𝗴𝗹𝗼𝗯𝗮𝗹 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗼𝗳 𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰 𝗮𝗻𝗮𝗹𝘆𝘀𝗶𝘀 𝗮𝗻𝗱 𝗺𝗼𝗱𝗲𝗹𝗹𝗶𝗻𝗴 𝘁𝗼𝗼𝗹𝘀 𝗳𝗼𝗿 𝗴𝗿𝗲𝗲𝗻 𝗮𝗻𝗱 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝘁𝗿𝗮𝗻𝘀𝗶𝘁𝗶𝗼𝗻𝘀. If you want a way to search the best available analytical tools in use by the world’s leading Finance Ministries and international organisations, search no further. Over a year’s worth of painstaking work as part of the Coalition of Finance Ministers Economic Analysis for Green and Resilient Transitions initiative has led to the Compendium of Practice - a global, collaborative resource with over 130 contributions from across 70 institutions, all downloadable as short papers. The Compendium showcases how Ministries of Finance (MoFs) and their partners are tackling key climate policy challenges through applied tools, modeling approaches, and capacity-building strategies. It covers:  1. The pressing questions many MoFs face in driving the transition   2. The plethora of economic analysis tools available to help address these questions from climate-enhanced macro modelling tools and physical climate risk models to decision-making frameworks and ex-post assessments of policy impacts.   3. The ways in which MoFs are building their own analytical capabilities and the premier capacity building efforts led by international organisations. Importantly, this is an open resource, 𝗱𝗲𝘀𝗶𝗴𝗻𝗲𝗱 𝘁𝗼 𝗯𝗲 𝘀𝗵𝗮𝗿𝗲𝗱. You are encouraged to use it, circulate it within your institutions, and pass it along through your networks. 𝗖𝗼𝗻𝘁𝗿𝗶𝗯𝘂𝘁𝗼𝗿𝘀 – 𝗽𝗹𝗲𝗮𝘀𝗲 𝗱𝗼 𝘀𝗵𝗮𝗿𝗲 𝘆𝗼𝘂𝗿 𝗳𝗮𝗻𝘁𝗮𝘀𝘁𝗶𝗰 𝗽𝗮𝗽𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝘀𝗲𝘃𝗲𝗿𝗮𝗹 𝗲𝘅𝗮𝗺𝗽𝗹𝗲𝘀 𝗶𝗻 𝘁𝗵𝗲 𝗰𝗼𝗺𝗺𝗲𝗻𝘁𝘀 𝗯𝗲𝗹𝗼𝘄 𝘁𝗼 𝘀𝗲𝗿𝘃𝗲 𝗮𝘀 𝗶𝗻𝘀𝗽𝗶𝗿𝗮𝘁𝗶𝗼𝗻. To make this collective knowledge widely accessible, a new standalone 𝘄𝗲𝗯𝘀𝗶𝘁𝗲 has been launched: The Macroeconomics of Green and Resilient Transitions website. This platforms complements the Coalition’s main site by making these contributions easy to explore, access, and apply—offering a practical gateway to policy-relevant tools and real-world examples. On the website, you’ll find:  • A searchable Compendium organized by policy questions, analytical tools, and capacity-building theme  • Full contributions showcasing country examples, practical guidance, and tools—typically 2–10 pages—available as individual downloads and to share on social media  • Access to the full reports of the Coalition of Finance Ministers for Climate Action HP4 Economic Analysis for Green and Resilient Transitions initiative  • Resources on the global community of practice Visit the website: 𝘄𝘄𝘄.𝗴𝗿𝗲𝗲𝗻𝗮𝗻𝗱𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁𝗲𝗰𝗼𝗻𝗼𝗺𝗶𝗰𝘀.𝗼𝗿𝗴 Coalition of Finance Ministers for Climate Action Mads Dalum Libergren Sam Koojo June A. Clare Nyakahuma Ralien Bekkers Leandro Rossi Frank van Lerven Grantham Research Institute on Climate Change & the Environment

Right now, a handful of economists you've never heard of are deciding the fate of billions in tax policy—and they're doing it behind closed doors with no appeals process. Welcome to the opaque world of "tax scoring," where a single comma can kill a $50 billion provision and most executives have no idea how the game is really played. Here's how the game really works—and why it matters for every business leader watching this process. The Scoring System That Controls Everything Think of "scoring" as the price tag Congress puts on every tax idea. The Joint Committee on Taxation (JCT) decides what each provision "costs" the government over a 10-year window. Higher score = higher chance your policy dies. But here's the crucial detail: JCT only looks at 10 years. Tax break that kicks in Year 11? Doesn't count. Benefit in Year 15? Free. This timing quirk shapes everything about how tax policy gets written. A Real Example Playing Out Right Now There's a provision being scored today that would let W-2 earners, retirees, and anyone with savings—not just capital gains investors—participate in Opportunity Zone investing. The House wrote this as a "deferral" (like a 401k) capped at $10,000 per year. Why such a tiny cap? Because under JCT methodology, if you offer a tax deferral, they often assume every eligible taxpayer will use it. That projects to billions in cost. But there's a much smarter approach: Let people invest after-tax dollars with no deferral, but still receive tax-free growth after 10 years. Think "Roth IRA for community investment." The result? Since there's no deferral and the benefit occurs after Year 10, this version should score at $0. Same policy impact. Zero budget cost. Why This Matters Beyond Tax Policy While everyone watches the political theater, the real action is technical teams fighting over: • Timing assumptions • Behavioral models • Definitional details • Which benefits "count" As one Hill staffer told me: "You don't write tax law—you write what JCT will score." This dynamic affects every major policy initiative—from R&D credits to infrastructure incentives to retirement savings rules. The difference between policy that passes and policy that dies often comes down to a single word, a comma, or whether JCT sees a benefit in Year 10 versus Year 11. Understanding the scoring process—its methodologies, assumptions, and blind spots—is essential for effective advocacy. The most elegant policy solutions often emerge not from political compromise, but from technical precision that aligns good policy with favorable scoring. The Opportunity Zone provision is a perfect test case. Will the Senate recognize that smart structuring can unlock billions in community investment at zero budget cost? We'll find out soon. What other "inside baseball" policy processes would be valuable to decode for the business community?