Causal Inference on Discrete Data

In the first part of the thesis, we consider bivariate causal inference on different discrete data settings—univariate i.i.d., univariate non-i.i.d., and multivariate i.i.d. pairs. To this end, we build upon the principle of algorithmic independence of conditionals (AIC), which states that marginal distribution of the cause is algorithmically independent of conditional distribution of the effect given the cause. However, as Kolmogorov complexity is not computable, we approximate the AIC from above through the statistically sound Minimum Description Length (MDL) principle. On univariate i.i.d. and non-i.i.d. pairs, where causal mechanisms are simple, we use refined MDL codes that are minimax optimal w.r.t. a model class. We resort to crude MDL codes on a pair of multivariate i.i.d. variables.
Although useful, saying that there exists a causal relationship from a set of variables towards a certain variable of interest does not always fully satisfy one’s curiosity; for a domain expert it is of particular interest to know those conditions that are most effective, such as the combinations of drugs and their dosages that are most effective towards recovery. Motivated by this problem, in the second part of this thesis, we consider discovering statistically reliable causal rules from observational data. Overall, extensive evaluations show that methods proposed in this thesis are highly accurate, and discover meaningful causations from real-world data.