CHAI will address four fundamental research challenges:

• Causality for ML | Enhancing robustness of predictive models against heterogeneity, distribution shifts, measurement error (noise) and fairness issues by exploiting causal principles.

• Large scale causal discovery | Inferring invariances to domain shifts and interventions across large and diverse data regimes and missingness patterns (sparsity) to inform causal models.

• Causal representation learning | Building deep representations from unstructured data (e.g. images, text) optimised for detecting effects of interventions and data and domain shifts.

• Causal reasoning for decision support | Deriving optimal policies integrating actions, prediction under interventions and risk modelling accounting for data-constrained uncertainty.