I hold a PhD in Economics from the University of Zürich, specializing in industrial organization, econometrics and competition policy.

I believe in knowledge sharing, and I try to contribute by sharing my code, tutorials and lecture notes. I write on Medium for Towards Data Science.

Posts

Understanding CUPED

An in depth guide to the state-of-the art variance reduction technique in A/B tests During my PhD, I spent a lot of time learning and applying causal inference methods to experimental and observational data.

Apr 24, 2024 15 min read

Clustered Standard Errors in AB Tests

What to do when the unit of observation differs from the unit of randomization A/B tests are the golden standard of causal inference because they allow us to make valid causal statements under minimal assumptions, thanks to randomization.

Mar 17, 2024 15 min read

Understanding Group Sequential Testing

How to run valid experiments, with unplanned peeking and early stopping. A/B tests are the golden standard of causal inference because they allow us to make valid causal statements under minimal assumptions, thanks to randomization.

Dec 26, 2023 18 min read

Should we ever use DiD?

Assume parallel trends. DiD nails the ATT. Would a double-machine learning (DML) estimator also be able to recover the ATT? And the ATE and ATC? When should we use DiD if we have DML?

Nov 21, 2023 13 min read

Instrumental Variables

How to estimate causal effects when you cannot randomize treatment. A/B tests are the golden standard of causal inference because they allow us to make valid causal statements under minimal assumptions, thanks to randomization.

Nov 12, 2023 14 min read

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Newsletter

XYZ#5

A causal inference newsletter News The Causal AI Conference 2023 Two weeks ago, CausalLens hosted one of the most important causal inference conferences, especially in the industry. The full conference is available on YouTube.

Oct 9, 2023

XYZ#4

A causal inference newsletter News Encouragement Designs and Instrumental Variables for A/B Testing Spotify researchers introduce IV for industry applications, when randomizing the treatment is not possible or desirable, for example because the feature is common knowledge.

Aug 27, 2023

XYZ#3

A causal inference newsletter News Speeding up HelloFresh’s Bayesian AB Testing PyMC researchers tested different ideas to speed up thousands of concurrent AB tests. First, they decreased model parametrization, and sampling length, leading to marginal speed increases.

Aug 20, 2023

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Projects

Algorithmic Collusion on Online Marketplaces

I show that online marketplaces, by algorithmically controlling consumers’ attention, have the incentives and ability to either facilitate or prevent algorithmic collusion, depending on their business model. I also explore platforms’ dual role.

Foreclosure Complementarities

We use a computational model of market dynamics to investigate the interplay between exclusionary bundling and predatory pricing. We show that the two foreclosure practices are complementary and we investigate potential policy interventions.

Algorithmic Collusion Detection

I show that algorithms can learn reward-punishment schemes that are fully independent from the rival’s actions and I propose a model-free test for algorithmic collusion based on historical data.

Approximation Methods for Large Dynamic Stochastic Games

I compare existing approximation methods to compute Markow Perfect Equilibrium in dynamic stochastic games with large state spaces. I also propose a new approximation method called “Games with Random Order”.

Ownership and Product Similarity

I generate a time-varying measure of S&P500 firm similarity using a zero-shot clustering model. The model takes as input BERT embeddings of product descriptions and is trained on market definitions from the EU commission. The objective is to estimate the causal effect of common ownership on product similarity.

Contact

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