Towards integrative causal analysis of heterogeneous data sets and studies

Ioannis Tsamardinos, Sofia Triantafillou, Vincenzo Lagani

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


We present methods able to predict the presence and strength of conditional and unconditional dependencies (correlations) between two variables Y and Z never jointly measured on the same samples, based on multiple data sets measuring a set of common variables. The algorithms are specializations of prior work on learning causal structures from overlapping variable sets. This problem has also been addressed in the field of statistical matching. The proposed methods are applied to a wide range of domains and are shown to accurately predict the presence of thousands of dependencies. Compared against prototypical statistical matching algorithms and within the scope of our experiments, the proposed algorithms make predictions that are better correlated with the sample estimates of the unknown parameters on test data ; this is particularly the case when the number of commonly measured variables is low. The enabling idea behind the methods is to induce one or all causal models that are simultaneously consistent with (fit) all available data sets and prior knowledge and reason with them. This allows constraints stemming from causal assumptions (e.g., Causal Markov Condition, Faithfulness) to propagate. Several methods have been developed based on this idea, for which we propose the unifying name Integrative Causal Analysis (INCA). A contrived example is presented demonstrating the theoretical potential to develop more general methods for co-analyzing heterogeneous data sets. The computational experiments with the novel methods provide evidence that causallyinspired assumptions such as Faithfulness often hold to a good degree of approximation in many real systems and could be exploited for statistical inference. Code, scripts, and data are available at © 2012 Ioannis Tsamardinos, Sofia Triantafillou and Vincenzo Lagani.
Original languageEnglish (US)
Pages (from-to)1097-1157
Number of pages61
JournalJournal of Machine Learning Research
StatePublished - Apr 1 2012
Externally publishedYes

Bibliographical note

Generated from Scopus record by KAUST IRTS on 2023-09-23

ASJC Scopus subject areas

  • Artificial Intelligence
  • Software
  • Statistics and Probability
  • Control and Systems Engineering


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