Program:
Monday
Session I
Maximilian Kasy, “Adaptive treatment assignment in experiments for policy choice”
Bezirgen Veliyev, “Functional Sequential Treatment Allocation”
Keynote
Uri Shalit about “Machine learning and causal inference: a two-way road”:
"This talk will have two parts. In the first we will discuss a framework we developed for learning individualized treatment recommendations from observational health data, merging ideas from machine learning and causal inference. We will see examples of our framework applied to two crucial health problems using data from tens of thousands of patients, and discuss some important causal-inference challenges that come to focus in this setting. In the second part we will show how we use ideas from the causal inference literature to address long standing problems in machine learning: off-policy evaluation in a partially observable Markov decision process (POMDP), and learning predictive models that are stable against distributional shifts."
Session II
Claudio Schilter, “Dynamically Optimal Treatment Allocation Using Reinforcement Learning”
Bart Cockx, “Priority to immigrants? Heterogeneous effects of training programmes for unemployed in Belgium”
Daniel Jacob, “Does Tenure make you love your Job?”
Nicolaj Mühlbach, “Heterogeneous Treatment Effects of an Early Retirement Reform”
Tuesday
Session III
Dmitry Arkhangelsky, “Double-Robust Identification for Causal Panel Data Models”
Martin Spindler, “Uniform Inference in High-Dimensional Gaussian Graphical Models”
Keynote
Stefan Wager about “Designing Loss Functions for Causal Machine Learning”:
"Given advances in machine learning over the past decades, it is now possible to accurately solve difficult non-parametric prediction problems in a way that is routine and reproducible. Moreover, following the recent line of work on orthogonalized estimation, it is now also well understood how to use these tools for powerful estimation of low-dimensional causal parameters in a semiparametric setting. In this talk, I'll discuss how these ideas can be extended to less structured problems, such as heterogeneous treatment effect estimation and policy learning, where the target of inference is not a single low-dimensional parameter. In particular, I'll focus on understanding how ideas from semiparametric estimation can be used to design task-appropriate loss functions that can then be deployed in a machine learning pipeline."
Session IV
Michael Zimmert, “Nonparametric estimation of causal heterogeneity under high-dimensional confounding”
Helmut Farbmacher, “Instrument Validity Tests with Causal Trees”
Marica Valente, “Changing the incentive to pollute: Heterogeneous effects of waste pricing policies”
Andreas Gulyas, “Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach”
Poster Session 1:
1. Daniel Goller, “Causal machine learning increases the value of microeconometric evaluation studies: The case of German active labour market policies for long term unemployed”
2. Hyunseok Jung, “Network Competition and Team Chemistry in the NBA”
3. Thomas Klausch, “Estimating the policy value of optimal treatment regimens using cross-fitting”
4. Noemi Kreif, “An application of causal machine learning to explore heterogeneous treatment effects of social health insurance programmes in Indonesia”
5. Gabriel Okasa, “The Effect of Sport in Online Dating: Evidence from Causal Machine Learning”
6. Milena Suarez Castillo, “Disentangling the causal effect of air pollutants effects on health by mining a large set of instruments”
7. Costanza Tortu, “Causal Trees for Heterogeneous Treatment and Spillover Effects under Clustered Network Interference”
8. Marina Töpfer, “The Gender Pay Gap Revisited: Does Machine-Learning over New Insights?”
Poster Session 2:
1. Martin Biewen, “Revisiting Angrist and Evans (1998) using instrumental variable forests with more than one instrument”
2. Konstantin Görgen, “Evaluating Effects of Tuition Fees: Lasso for the Case of Germany”
3. Philipp Heiler, “Efficient Covariate Balancing for the Local Average Treatment Effect”
4. Martin Huber, “Causal mediation analysis with double machine learning”
5. Sophie-Charlotte Klose, “Identifying Latent Structures in Maternal Employment: Evidence on the German Parental Benefit Reform”
6. Michael Knaus, “An Evaluation of Swiss Active Labor Market Policies based on Double Machine Learning”
7. Elias Moor, “Machine Learning Based Estimation of Average Treatment Effects under Unconfoundedness”
8. Tony Strittmatter, “Can Targeting of Solicitation Letters Increase Charitable Aid? An Efficient Policy Learning Approach”