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.
How I Think
I work at the intersection of three things: science (causal inference, decision theory, economic modeling), engineering (production systems, automation, scalable infrastructure), and judgment (knowing what matters and when enough is enough).
Most organizations don't lack insight — they lack the ability to act on it. The bottleneck is never the model. It's the dependencies between knowing something and doing something about it: the missing pipeline, the manual process, the analysis that lives in a notebook instead of a system. I remove those dependencies.
Every project I take on follows the same logic: take established science, productionize it, automate it, and scale it — then build a learning loop so each cycle compounds. The goal isn't better analysis. It's faster, evidence-backed decisions.
Showcase
My projects follow the same pattern. Productionize established science, wire it into an automated decision loop, and let each cycle sharpen the next. The showcase application draws from causal inference and statistical decision theory, but the approach is general and the architecture portable to any domain where organizations must learn from evidence and act under uncertainty.
Impact Engine
A decision loop that turns causal inference into repeatable action. Measure, Evaluate, Allocate, Scale — automatically, every cycle.
Track Record
- 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 back into investment decisions through iterative refinement. Available as Working Paper: Science-backed Decisions for High Stakes Investments Under Uncertainty.
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.
Selected 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.