Patrick Burauel

Patrick Burauel

post-doctoral scholar

California Institute of Technology

Biography

I’m a postdoc at Caltech working on causality with Frederick Eberhardt. I’m interested in how to address confounding in non-experimental data and how to aggregate causal variables. Though the methods we develop are domain-general, I focus on applications in economics. In my doctoral dissertation I explore synergies between research on causality in machine learning and economics. You can reach me at pburauel [at] caltech [dot] edu.

You can download my CV here.

Interests

  • Machine Learning
    for Causal Inference
  • Causal Structure Discovery
  • Causal Representation Learning

Education

  • PhD in Economics, 2020

    Free University Berlin / Berlin School of Economics

  • MSc in Economics, 2015

    Paris School of Economics and Ecole Polytechnique

Current Projects

Causal Feature Learning to learn macro-economic variables

Finding proximate causes of economic growth potential using complex economic trade relationships by using Causal Feature Learning (empirical basis: yearly bilateral trade data for 180 countries, 7000 product categories, 50 years). Thereby, we provide an empirical test for the Economic Complexity Index. Joint with Frederick Eberhardt (Caltech)


Deconfounding using the Information Bottleneck principle

Developing methods to disentangle causal from spurious factors of variation in observational data by leveraging machine learning tools (artificial neural networks and the information bottleneck principle) and known markers of confounding in observational data.


Novel Formalizations of the Principle of Independent Mechanisms

Developing a novel, formal interpretation of the Principle of Independent Mechanisms (causal, unlike spurious, relations are modular) to measure confounding in observational data. Joint with Michel Besserve (Max Planck Institute for Intelligent Systems)

Job Market Paper

Evaluating Instrument Validity using the Principle of Independent Mechanisms

published at Journal of Machine Learning Research

Abstract: The validity of instrumental variables to estimate causal effects is typically justified narratively and often remains controversial. Critical assumptions are difficult to evaluate since they involve unobserved variables. Building on Janzing and Schölkopf’s (2018) method to quantify a degree of confounding in multivariate linear models, we develop a test that evaluates instrument validity without relying on Balke and Pearl’s (1997) inequality constraints. Instead, our approach is based on the Principle of Independent Mechanisms, which states that causal models have a modular structure. Monte Carlo studies show a high accuracy of the procedure. We apply our method to two empirical studies: first, we can corroborate the narrative justification given by Card (1995) for the validity of college proximity as an instrument for educational attainment to estimate financial returns to education. Second, we cannot reject the validity of past savings rates as an instrument for economic development to estimate its causal effect on democracy (Acemoglu et al., 2008).


Working Papers

Testability of Reverse Causality Without Exogenous Variation

with Christoph Breunig, submitted to The Econometrics Journal

Abstract: This paper shows that testability of reverse causality is possible even in the absence of exogenous variation, such as in the form of instrumental variables. Instead of relying on exogenous variation, we achieve testability by imposing relatively weak model restrictions and exploiting that a dependence of residual and purported cause is informative about the causal direction. Our main assumption is that the true functional relationship is nonlinear and that error terms are additively separable. We extend previous results by incorporating control variables and allowing heteroskedastic errors. We build on reproducing kernel Hilbert space (RKHS) embeddings of probability distributions to test conditional independence and demonstrate the efficacy in detecting the causal direction in both Monte Carlo simulations and an application to German survey data.

link to arxiv version


The German Minimum Wage and Wage Growth: Heterogeneous Treatment Effects Using Causal Forests

with Carsten Schröder

Abstract: Previous research suggests that minimum wages induce heterogeneous treatment effects on wages across different groups of employees. This research usually defines groups ex ante. We analyze to what extent effect heterogeneities can be discerned in a data-driven manner by adapting the generalized random forest implementation of Athey et al (2019) in a difference-in-differences setting. Such a data-driven methodology allows detecting the potentially spurious nature of heterogeneities found in subgroups chosen ex-ante. The 2015 introduction of a minimum wage in Germany is the institutional background, with data of the Socio-economic Panel serving as our empirical basis. Our analysis not only reveals considerable treatment heterogeneities, it also shows that previously documented effect heterogeneities can be explained by interactions of other covariates.

link to SSRN