Our goal is to identify beneficial interventions from observational data. We consider interventions that are narrowly focused (impacting few features) and may be tailored to each individual or globally enacted over a population. If the underlying relationship obeys an invariance condition, our approach can identify beneficial interventions directly from observational data, side-stepping causal ...

conditional  methods  of adjustment  are often used to quantify  the effect  of the exposure on the outcome.  as  a  result,  the  stratums-specific  risk  ratio  estimates  are  reported  in  the  presence  of interaction   between   exposure  and  confounder(s)   in  the  literature,  even  if  the  target  of  the intervention on the exposure is the total population and the interaction itsel...

We consider three general classes of data-driven statistical tests. Neyman’s smooth tests, data-driven score tests and data-driven score tests for statistical inverse problems serve as important special examples for the classes of tests under consideration. Our tests are additionally incorporated with model selection rules. The rules are based on the penalization idea. Most of the optimal penal...

Recent advances in causal inference have given rise to a general and easy-to-use estimator for assessing the extent to which the effect of one variable on another is mediated by a third. This estimator, called Mediation Formula, is applicable to nonlinear models with both discrete and continuous variables, and permits the evaluation of path-specific effects with minimal assumptions regarding th...

The development of the civil aviation industry has continuously increased requirements for efficiency airport ground support services. In existing research, there not yet been a process model that directly obtains from log to study causal relationship between service nodes and flight delays. Most studies mainly use machine learning methods predict delays, they are based on is an ideal model. di...