  Predicting the chemical properties of compounds is crucial in discovering
novel materials and drugs with specific desired characteristics. Recent
significant advances in machine learning technologies have enabled automatic
predictive modeling from past experimental data reported in the literature.
However, these datasets are often biased because of various reasons, such as
experimental plans and publication decisions, and the prediction models trained
using such biased datasets often suffer from over-fitting to the biased
distributions and perform poorly on subsequent uses. Hence, this study focused
on mitigating bias in the experimental datasets. We adopted two techniques from
causal inference combined with graph neural networks that can represent
molecular structures. The experimental results in four possible bias scenarios
indicated that the inverse propensity scoring-based method and the
counter-factual regression-based method made solid improvements.
