Causal inference and discovery
A relatively recent addition to the arsenal of machine learning, causal inference is growing to become a powerful tool for the phenomenological understanding of natural and artificial phenomena. In particular, it provides means to estimate the effects of interventions from mere observations thus providing invaluable help for automated decision-making, offline RL and multiple other fields.

Research feed

Seminar

A soft introduction to causal inference

‘Correlation does not imply causation’ is a famous saying in statistics. The goal of many (scientific) studies is to draw conclusions on …

Lukas Dreier
Causal Inference and Discovery

Other series in Trustworthy and interpretable ML

Classifier calibration

For many applications of probabilistic classifiers it is important that the predicted confidence vectors reflect true probabilities (one …

Trustworthy and Interpretable ML

Explainable AI

Large opaque models like neural networks require dedicated methods to study and interpret their behavior. In this series we review recent …

Trustworthy and Interpretable ML

Simulation-based Inference

Simulation-based inference (SBI) offers a powerful framework for Bayesian parameter estimation in intricate scientific simulations where …

Trustworthy and Interpretable ML

Uncertainty Quantification

Uncertainty quantification (UQ) in machine learning is the practice of measuring or estimating uncertainty in models. It is a set of tools …

Trustworthy and Interpretable ML

Probabilistic Models

Uncertainty permeates all aspects of real-world agency: Perception is subject to uncertainty owing to partial observability and unreliable …

Trustworthy and Interpretable ML
Check all of our work