Abhinav Gorantla

Shu Wan, Abhinav Gorantla, Huan Liu et al.

Under standard graphical assumptions, the Markov boundary of a target variable is the smallest set of features that renders every other feature redundant. Once the boundary is observed, the target is conditionally independent of the rest of the table. This is a tempting object for tabular prediction, since it names exactly the columns a model should need. Yet modern regressors are still trained on the full feature set. We ask whether the Markov boundary is genuinely useful for prediction on SCM3K, a 3,450-task synthetic SCM benchmark with feature counts from 40 to 1000 and six SCM families, evaluated with six regressors. The answer is more nuanced than the theory suggests. Restricting a regressor to the oracle boundary often improves prediction substantially, and the improvement grows as the feature space becomes larger and sparser. But the natural pipeline of recovering the boundary with causal discovery and training on the recovered mask does not deliver. Existing estimators exhaust the compute budget before reaching the regime where the boundary helps most, and even where they run they rarely beat the full feature set. We trace this to three causes. Discovery optimizes structural recovery rather than prediction. False negatives and false positives carry sharply asymmetric predictive cost. The exact boundary is only one of many feature sets that beat all features. We then develop what these facts imply for prediction-aligned feature selection and for tabular models that learn to use causal structure.

Pratanu Mandal, Abhinav Gorantla, K. Selçuk Candan et al.

Skyline queries are popular and effective tools in multi-criteria decision support as they extract interesting (pareto-optimal) points that help summarize the available data with respect to a given set of preference attributes. Unfortunately, the efficiency of the skyline algorithms depends heavily on the underlying data statistics. In this paper, we argue that the efficiency of the skyline algorithms could be significantly boosted if one could erase any attribute correlations that do not agree with the preference criteria, while preserving (or even boosting) correlations that agree with the user provided criteria. Therefore, we propose a causallyinformed selective de-correlation mechanism to enable skyline algorithms to better leverage the pruning opportunities provided by the positively-aligned data distributions, without having to suffer from the mis-alignments. In particular, we show that, given a causal graph that describes the underlying causal structure of the data, one can identify a subset of the attributes that can be used to selectively de-correlate the preference attributes. Importantly, the proposed causal search for skylines (CSS) approach is agnostic to the underlying candidate enumeration and pruning strategies and, therefore, can be leveraged to improve any popular skyline discovery algorithm. Experiments on multiple real and synthetic data sets and for different skyline discovery algorithms show that the proposed causally-informed selective de-correlation technique significantly reduces both the number of dominance checks as well as the overall time needed to locate skyline points.

Ahmet Kapkiç, Pratanu Mandal, Shu Wan et al.

While witnessing the exceptional success of machine learning (ML) technologies in many applications, users are starting to notice a critical shortcoming of ML: correlation is a poor substitute for causation. The conventional way to discover causal relationships is to use randomized controlled experiments (RCT); in many situations, however, these are impractical or sometimes unethical. Causal learning from observational data offers a promising alternative. While being relatively recent, causal learning aims to go far beyond conventional machine learning, yet several major challenges remain. Unfortunately, advances are hampered due to the lack of unified benchmark datasets, algorithms, metrics, and evaluation service interfaces for causal learning. In this paper, we introduce {\em CausalBench}, a transparent, fair, and easy-to-use evaluation platform, aiming to (a) enable the advancement of research in causal learning by facilitating scientific collaboration in novel algorithms, datasets, and metrics and (b) promote scientific objectivity, reproducibility, fairness, and awareness of bias in causal learning research. CausalBench provides services for benchmarking data, algorithms, models, and metrics, impacting the needs of a broad of scientific and engineering disciplines.