Causal ML Lab
We develop new machine learning methods for causal inference and decision-making.

We develop new machine learning methods for causal inference and decision-making.
Existing machine learning (ML) methods typically model correlations in the data, but not causal relationships. Such correlation-based methods can have suboptimal performance and even lead to systematic errors. To address this, we aim to develop novel ML models for causal inference, i.e., that are able to infer causal effects from observational data. Concretely, we advance causal ML toward the directions of flexibility, efficiency, and robustness:
Moreover, we aim to achieve better visibility and further applicability of our research through principles of (i) Open-source development and (ii) Real-world applications.
We seek to publish in the top-tier outlets of ML research (e.g., NeurIPS, ICML, ICLR) across different directions:
(1.1) Causal ML for multi-modal data
(1.2) Causal ML for time series data
(1.3) Constrained causal ML:
(1.4) Causal ML for heterogeneous data sources:
Future directions: (1.5) Bandits and reinforcement learning
(2.1) Model-agnostic methods:
(2.2) State-of-the-art deep learning models:
Future directions: (2.3) Foundation models for Causal ML
(3.1) Robustness to violation of assumptions of causal ML
(3.2) Uncertainty quantification for causal ML
Additionally, we have several applied works: