  Aerosol-cloud interactions include a myriad of effects that all begin when
aerosol enters a cloud and acts as cloud condensation nuclei (CCN). An increase
in CCN results in a decrease in the mean cloud droplet size (r$_{e}$). The
smaller droplet size leads to brighter, more expansive, and longer lasting
clouds that reflect more incoming sunlight, thus cooling the earth. Globally,
aerosol-cloud interactions cool the Earth, however the strength of the effect
is heterogeneous over different meteorological regimes. Understanding how
aerosol-cloud interactions evolve as a function of the local environment can
help us better understand sources of error in our Earth system models, which
currently fail to reproduce the observed relationships. In this work we use
recent non-linear, causal machine learning methods to study the heterogeneous
effects of aerosols on cloud droplet radius.
