Fig. 1: The overall model illustration and performance of the proposed AutoCI.
From: Automated causal inference in application to randomized controlled clinical trials
Top: an illustrative scheme of the proposed AutoCI. In the syntax (top left), the type T includes atomic type (ATM), function type (FUC) and abstract data type (ADT), the program prg contains neural network (NN), function composition (COMP), concatenation (CAT), filter (FILTER), predicate (PRED) and so on. In the causal differentiable learning, causal prob. indicates causal probability. The outcome variable RFS means recurrence free survival. Bottom left: the sampled numbers of type-safe functions versus generic functions. Here the size is the maximum amount of nn and PRED functions allowed during the program synthesis. Bottom middle: the learning curve of the JS for top-four type-safe functions achieved in the case with pathological, molecular and immune variables. Bottom right: the running time of determining the causal variables for P, PM and PMI. Here the proposed AutoCI utilizes the function COMP(NN, CAT(FILTER(PRED))).
