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Identify treatment effect patterns for personalised decisions
The existing interpretable modelling methods take a top-down approach to search for subgroups with heterogeneous treatment effects and they may miss the most specific and relevant context for an individual.
Discovering Context Specific Causal Relationships
With the increasing need of personalised decision making, such as personalised medicine and online recommendations, a growing attention has been paid to the discovery of the context and heterogeneity of causal relationships.
Mining Combined Causes in Large Data Sets
A straightforward approach to uncovering a combined cause is to include both individual and combined variables in the causal discovery using existing methods, but this scheme is computationally infeasible due to the huge number of combined variables.
From Observational Studies to Causal Rule Mining
Specifically, association rule mining can be used to deal with the high-dimensionality problem while observational studies can be utilised to eliminate non-causal associations.
Causal Decision Trees
Classification methods are fast and they could be practical substitutes for finding causal signals in data.
