Improving Causal Effect Estimation of Weighted RegressionBased Estimator using Neural Networks
This work addresses a domain-specific challenge in causal inference for applications like medical treatment selection and policy-making, offering incremental improvements over existing regression-based estimators.
The paper tackles the problem of estimating causal effects from observational data with high dimensionality and limited samples by introducing a neural network-based estimator, which improves solution quality by up to 55% compared to state-of-the-art methods.
Estimating causal effects from observational data informs us about which factors are important in an autonomous system, and enables us to take better decisions. This is important because it has applications in selecting a treatment in medical systems or making better strategies in industries or making better policies for our government or even the society. Unavailability of complete data, coupled with high cardinality of data, makes this estimation task computationally intractable. Recently, a regression-based weighted estimator has been introduced that is capable of producing solution using bounded samples of a given problem. However, as the data dimension increases, the solution produced by the regression-based method degrades. Against this background, we introduce a neural network based estimator that improves the solution quality in case of non-linear and finitude of samples. Finally, our empirical evaluation illustrates a significant improvement of solution quality, up to around $55\%$, compared to the state-of-the-art estimators.