Philipp Eisenhauer
I build production systems that translate scientific models into actionable decision frameworks for high-stakes business problems under uncertainty. Working at the intersection of statistical modeling, economic reasoning, and systems engineering, I create solutions that enable organizations to move from analysis to action—combining scientific rigor with practical judgment to bridge the gap from insight to impact at scale.
Business Impact
- Impact API – Impact Measurement Toolkit: Building a production-grade platform that exposes impact-measurement tooling and causal models through a unified API. Designed to embed standardized, experiment-backed evaluations directly into agentic workflows. Emphasizes automation, rigorous diagnostics, and broad adoption across business teams.
- Catalog AI – Causal Impact Framework: Developed the causal impact measurement framework for Amazon's Catalog AI initiative, leveraging randomized experiments to quantify the business value of product data improvements. Built production pipelines that measure customer impact, guide model development, and drive seller adoption with experiment-backed insights. Published in Harvard Business Review: Inside Amazon's AI Factory.
- Uncertain Decisions – Portfolio Allocation Framework: Designed a standardized framework to evaluate the return on investment (ROI) of major tech initiatives by aligning assumptions across teams, validating inputs, and conducting sensitivity and scenario analyses. Established ongoing tracking processes that refine projections and feed ROI data into iterative improvements. Available as Working Paper: Science-backed Decisions for High Stakes Investments Under Uncertainty.
Business Experience
Amazon.com
Science lead for impact measurement platforms serving Amazon's product catalog and AI initiatives. Develop and deploy production systems for experiment design, causal inference, and return on investment (ROI) evaluation that guide high-stakes investments.
eisenhauer.io
Building production-grade platforms that translate scientific models into decision-ready systems. Co-designing models and infrastructure simultaneously to enable rapid iteration from insight to operational impact at scale. Focus areas include impact-measurement platforms, causal inference engines, and robust decision frameworks.
Academic Appointments
University of Washington
Teaching: Instructing a course on causal data analysis and scientific computing in the Department of Economics, cross-listed with the Computer Science and Statistics departments.
University of Bonn
Research Agenda: Advanced decision-making under uncertainty through robust computational frameworks. Integrated statistical decision theory, robust optimization, and econometric modeling to develop tools that quantify and navigate uncertainty in dynamic systems. Applications included policy design and investment strategies, emphasizing practical, data-driven solutions to complex real-world problems.
Policy Agenda: Led a multidisciplinary project combining economic, epidemiological, and financial models to quantify uncertainty and enhance decision-making. Supported by the university's Transdisciplinary Research Area and the Volkswagen Foundation, the initiative advanced robust decision frameworks with real-world policy applications.
Selected Publications
Business
- Eisenhauer, Philipp (2025). Science-backed Decisions for High Stakes Investments Under Uncertainty. Working Paper.
- Eisenhauer, Philipp, Thomke, Stefan, and Sahni, Puneet (2025). Inside Amazon's AI Factory. Harvard Business Review.
- Abraham Asfaw, Philipp Eisenhauer, and Andrea Scarinci (2024). Evaluating the Helpfulness of AI-enhanced Catalogue Data. Amazon Science Blog.
Academia
- Philipp Eisenhauer, Janoś Gabler, Lena Janys, and Christopher Walsh (2025). Structural Models for Policy-Making: Coping with Parametric Uncertainty. International Economic Review (conditionally accepted).
- Manudeep Bhuller, Philipp Eisenhauer, and Moritz Mendel (2025). Sequential Choices, Option Values, and the Returns to Education. Quantitative Economics (2nd resubmission).
- Maximilian Blesch and Philipp Eisenhauer (2021). Robust Decision-Making Under Risk and Ambiguity. arXiv.
- Philipp Eisenhauer, James J. Heckman, and Edward Vytlacil (2015). The Generalized Roy Model and the Cost-Benefit Analysis of Social Programs. Journal of Political Economy, 123(2), 413–443.
- Philipp Eisenhauer, James J. Heckman, and Stefano Mosso (2015). Estimation of Dynamic Discrete Choice Models by Maximum Likelihood and the Simulated Method of Moments. International Economic Review, 56(2), 331–357.
Teaching
- Data Science: Introduces fundamental microeconometric methods with a focus on making and evaluating causal claims. Covers techniques for applying and critically assessing causal inference methods. Emphasizes practical skills using Python, the SciPy ecosystem, and Jupyter Notebooks for data analysis and visualization.
- Scientific Computing: Focuses on advanced scientific computing for economic modeling, covering numerical methods, software engineering practices, and high-performance computing. Includes guest lectures linking theory to real-world applications and career opportunities. Delivered in a cloud environment for scalable and reproducible project development.