We consider testing and learning problems on causal Bayesian networks as defined by
Pearl (Pearl, 2009). Given a causal Bayesian network M on a graph with n discrete variables …

We consider basic conceptual questions concerning the relationship between statistical
estimation and causal inference. Firstly, we show how to translate causal inference …

We study the problem of efficiently estimating the effect of an intervention on a single
variable using observational samples. Our goal is to give algorithms with polynomial time …

Motivation Estimating causal queries, such as changes in protein abundance in response to
a perturbation, is a fundamental task in the analysis of biomolecular pathways. The …

We consider the problem of robust parameter estimation from observational data in the
context of linear structural equation models (LSEMs). Under various conditions on LSEMs …

We study the problem of efficiently estimating the effect of an intervention on a single
variable (atomic interventions) using observational samples in a causal Bayesian network …

We consider numerical stability of the parameter recovery problem in Linear Structural
Equation Model (LSEM) of causal inference. Numerical stability is essential for the …

55 just 20 percent of its patients. These patients represent the most costly clients, and many
of them are unprofitable from Stardust's point of view. Kamala's assignment is to understand …

We consider the problem of robust parameter estimation from observational data in the
context of linear structural equation models (LSEMs). Under various conditions on LSEMs …