Deception by Omission: Using Adversarial Missingness to Poison Causal
  Structure Learning
Deniz Koyuncu Alex Gittens Bülent Yener Moti Yung
Inference of causal structures from observational data is a key component of
causal machine learning; in practice, this data may be incompletely observed.
Prior work has demonstrated that adversarial perturbations of completely
observed training data may be used to force the learning of inaccurate causal
structural models (SCMs). However, when the data can be audited for correctness
(e.g., it is crytographically signed by its source), this adversarial mechanism
is invalidated. This work introduces a novel attack methodology wherein the
adversary deceptively omits a portion of the true training data to bias the
learned causal structures in a desired manner. Theoretically sound attack
mechanisms are derived for the case of arbitrary SCMs, and a sample-efficient
learning-based heuristic is given for Gaussian SCMs. Experimental validation of
these approaches on real and synthetic data sets demonstrates the effectiveness
of adversarial missingness attacks at deceiving popular causal structure
learning algorithms.