12 papers with code • 0 benchmarks • 1 datasets
However, current methods for training neural networks for counterfactual inference on observational data are either overly complex, limited to settings with only two available treatments, or both.
We elaborate on using importance sampling for causal reasoning, in particular for counterfactual inference.
Precision medicine involves answering counterfactual questions such as "Would this patient respond better to treatment A or treatment B?"
This manuscript proposes a general approach for querying a causal biological knowledge graph, and converting the qualitative result into a quantitative structural causal model that can learn from data to answer the question.