Philipp Eisenhauer
Science alone doesn't create value. Productionizing, automating, and scaling it does.
I build production systems that translate scientific models into actionable decision frameworks for high-stakes business problems under uncertainty.
Science × Engineering × Decision = Impact
I work at the intersection of science, engineering, and decision. The science is measurement and modeling — the methods that tell you what actually worked and why. The engineering is production systems, automation, and scalable infrastructure — what turns a method into something that runs reliably. The decision layer is knowing what to act on, what to defer, and when enough evidence is enough.
Every project follows the same pattern: screen established science for what applies, adapt it to the specific use case, productionize it into tested software, automate the workflow, and scale it across the organization. Each cycle feeds learnings back into the next. The goal isn't better analysis — it's faster, evidence-backed decisions.
Showcase
Impact Engine
A config-driven Python ecosystem that runs a full decision loop in a single call. It measures each initiative's causal impact, evaluates the strength of that evidence, allocates budget across the portfolio under uncertainty, and scales the winners — four independent packages, each with its own CI pipeline, wired by an orchestrator at runtime.
Explore the Impact Engine →Inside Amazon's AI Factory
Published account of the causal impact measurement framework I developed for Amazon's Catalog AI initiative. Leveraging randomized experiments to quantify the business value of product data improvements, the framework powers production pipelines that measure customer impact, guide model development, and drive seller adoption with experiment-backed insights.
Read in Harvard Business Review →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 directly inform high-stakes investment decisions.
University of Washington
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
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.
Publications
Business
- Philipp Eisenhauer (2025). Science-backed Decisions for High Stakes Investments Under Uncertainty. Working Paper.
- Philipp Eisenhauer, Stefan Thomke, and Puneet Sahni (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
- Business Decisions: Teaches students to connect data insights to organizational action through systematic impact measurement and resource allocation. Covers causal inference for business, experimentation design, and building decision systems that scale.
- Data Science: Develops the ability to make and critically evaluate causal claims using real-world data. Covers research design, program evaluation, and microeconometric methods with hands-on application in Python.
- Scientific Computing: Builds production-grade computational skills for solving complex economic and statistical problems. Covers numerical optimization, software engineering practices, and high-performance computing in cloud environments.