  Causal inference methods are widely applied in various decision-making
domains such as precision medicine, optimal policy and economics. Central to
causal inference is the treatment effect estimation of intervention strategies,
such as changes in drug dosing and increases in financial aid. Existing methods
are mostly restricted to the deterministic treatment and compare outcomes under
different treatments. However, they are unable to address the substantial
recent interest of treatment effect estimation under stochastic treatment,
e.g., "how all units health status change if they adopt 50\% dose reduction".
In other words, they lack the capability of providing fine-grained treatment
effect estimation to support sound decision-making. In our study, we advance
the causal inference research by proposing a new effective framework to
estimate the treatment effect on stochastic intervention. Particularly, we
develop a stochastic intervention effect estimator (SIE) based on nonparametric
influence function, with the theoretical guarantees of robustness and fast
convergence rates. Additionally, we construct a customised reinforcement
learning algorithm based on the random search solver which can effectively find
the optimal policy to produce the greatest expected outcomes for the
decision-making process. Finally, we conduct an empirical study to justify that
our framework can achieve significant performance in comparison with
state-of-the-art baselines.
