Continuous Treatment Effect Estimation Using Gradient Interpolation and
  Kernel Smoothing
Lokesh Nagalapatti Akshay Iyer Abir De Sunita Sarawagi
We address the Individualized continuous treatment effect (ICTE) estimation
problem where we predict the effect of any continuous-valued treatment on an
individual using observational data. The main challenge in this estimation task
is the potential confounding of treatment assignment with an individual's
covariates in the training data, whereas during inference 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 sampled
treatments and inferred counterfactual outcomes. We infer 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 downweigh high variance inferences. We evaluate our method
on five benchmarks and show that our method outperforms 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
responses are more accurate, and (2) adding them to the training data reduces
the distributional distance between the confounded training distribution and
test distribution where treatment is independent of covariates. Our proposed
method is model-agnostic and we show that it improves ICTE accuracy of several
existing models.