Continuous Treatment Effect Estimation Using Gradient Interpolation and Kernel Smoothing
AAAI 2024(2024)
摘要
We address the Individualized continuous treatment effect
(ICTE) estimation problem where we predict the effect of
any continuous valued treatment on an individual using ob-
servational data. The main challenge in this estimation task
is the potential confounding of treatment assignment with in-
dividual’s covariates in the training data, whereas during in-
ference ICTE requires prediction on independently sampled
treatments. In contrast to prior work that relied on regularizers
or unstable GAN training, we advocate the direct approach
of augmenting training individuals with independently sam-
pled treatments and inferred counterfactual outcomes. We in-
fer counterfactual outcomes using a two-pronged strategy: a
Gradient Interpolation for close-to-observed treatments, and
a Gaussian Process based Kernel Smoothing which allows
us to down weigh high variance inferences. We evaluate our
method on five benchmarks and show that our method out-
performs six state-of-the-art methods on the counterfactual
estimation error. We analyze the superior performance of our
method by showing that (1) our inferred counterfactual re-
sponses are more accurate, and (2) adding them to the train-
ing data reduces the distributional distance between the con-
founded training distribution and test distribution where treat-
ment is independent of covariates. Our proposed method is
model-agnostic and we show that it improves ICTE accuracy
of several existing models.
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