Estimating individual treatment effect: generalization bounds and algorithms

There is intense interest in applying machine learning to problems of causal inference in fields such as healthcare, economics and education. In particular, individual-level causal inference has important applications such as precision medicine... (read more)

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Results from the Paper


TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK RESULT BENCHMARK
Causal Inference IDHP Random Forest Average Treatment Effect Error 0.96 # 10
Causal Inference IDHP Causal Forest Average Treatment Effect Error 0.4 # 5
Causal Inference IDHP TARNet Average Treatment Effect Error 0.28 # 2
Causal Inference IDHP Balancing Neural Network Average Treatment Effect Error 0.42 # 6
Causal Inference IDHP k-NN Average Treatment Effect Error 0.79 # 8
Causal Inference IDHP Balancing Linear Regression Average Treatment Effect Error 0.93 # 9
Causal Inference IDHP Counterfactual Regression + WASS Average Treatment Effect Error 0.27 # 1

Methods used in the Paper