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Marco Valtorta mgv@cse.sc January 28, 2005

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Marco Valtorta mgv@cse.sc January 28, 2005

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  1. Causal Discovery from Medical Textual DataSubramani Mani and Gregory F. CooperProceedings of the AMIA annual fall symposium, 2000, pp.542--546. Hanley and Belfus Publishers, Philadelphia, PA.Available at: http://omega.cbmi.upmc.edu/~mani/pub/amia_fs2000.pdfPresentation for the Bayesian Networks Reading Club Marco Valtorta mgv@cse.sc.edu January 28, 2005

  2. Learning from Textual Data • Text is ubiquitous • Causal knowledge aids in planning and decision making • Because it supports manipulation • Causal relations may represent prior and tacit knowledge • Learning from textual data is a new and difficult area of research • “[T]he present paper reports the first investigation of causal knowledge discovery from text.”

  3. Causal BNs • A causal BN is a BN in which each arc is interpreted as a direct causal influence between parent and child node • Casual (why?) Markov condition: a variable is independent of its non-descendants given its parents • Causal (why?) faithfulness condition: variables are independent only if their independence is implied by the causal Markov condition • Statistical testing assumption: independence tests on a finite dataset are correct with respect to the underlying causal process that generated the dataset

  4. LCD Algorithm • Algorithm for finding causal links between pairs of variables • Assumes Markov condition, faithfulness condition, and statistical testing assumption, and an additional assumption

  5. Assumption 4 • Given measured variables X,Y, and Z, if Y causes Z, and Y and Z are not confounded (i.e., they do not have a common unmeasured cause), then one of the causal networks below must hold: • In case (1), X and Y are independent; in case (2), they are dependent due to X causing Y; and in cases (3) and (4), they are confounded

  6. Local Causal Discovery Consider three measured variables W,X, and Y. We will model the ways in which each pair can be causally related as in the tables above. The H variables are unmeasured (latent, hidden) variables. There are 96 ways in which W,X, and Y can interact. This is not a complete list, but Cooper argues that very little is lost. Based on: Cooper, Gregory F. “A Simple Constraint-Based Algorithm for Efficiently Mining Observational Databases for Causal Relationships.” Data Mining and Knowledge Discovery 1, 203-224 (1997).

  7. Three Tests • D(W, X) • or: Dependent(X,Y) • D(X,Y) • or: Dependent(Y,Z) • I(W,Y|X) • or: Independent(X, Z given Y)

  8. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . D-Separation Conditions for the 96 Causal Graphs Graphs 18, 19, and 20 are the only ones for which all three tests D(W,X), D(X,Y), and I(W,Y|X) hold. In each of the three graphs, X causes Y.

  9. The three Graphs for Which All Tests Are Positive

  10. Example: No Causal Link There is no causal link between Y causes Z. Independent (X, Z given Y) fails.

  11. LCD Pseudocode

  12. Extra Test • D(W,Y) • Dependent(X,Y) • Why? Redundancy, if I understand Cooper correctly.

  13. Limitations of LCD • Many causal networks are missed, e.g: • LCD only returns separate pairwise causal relationships, which may need to be assembled.

  14. Time Complexity • Not too bad, because only three variables at a time are considered • O(mnnr), where m is the number of cases in the database, n is the number of variables, and r is the number of variables such as X (W in Cooper’s paper), i.e., variables that have no cause (“acausal”) • O(mn) if “few” acausal variables and potential effects (like Z, or Y in Cooper’s paper) • Space complexity: O(mn), which is the size of the database

  15. Text Dataset • 2060 ICU discharge summaries (documents) • 1808 unique words appeared in the documents • Age, gender, and race appeared in 1611 documents and were considered causeless (“acausal”) • Each of the 1808 words was coded as present (1) or absent (0): in total 1811 variables • m=1611, n=1811, r=3 • only 18 variables of type Z (possible effects) were considered: nausea, cyrrhosis, dyspnea,…

  16. Results • One good causal relation was • recovered • One bad causal relation was • also obtained • A study from infant birth and • death records led to more • causal relations. • Ref.: Subramani Mani, Gregory F. Cooper “A Study in Causal Discovery • from Population-Based Infant Birth and Death Records.” • Proceedings of the AMIA Annual Fall Symposium, 1999, p315--319. • Hanley and Belfus Publishers, Philadelphia, PA.

  17. Suggested Improvements • Multi-word phrases • More records • Multivariate causes or effects (?) • Encoding variable-value pairs (e.g.: serum sodium = high) (?) • The number of occurrences of phrases in a documents • The location of phrases in a document

  18. Limitations of the Text Study • Words, not phrases • Present or absent only • Context of a word is not considered • “hypertensive” and “not hypertensive” are treated the same way! • Synonyms are treated as different words • More generally: no linguistic analysis, no domain (semantic, ontological?) information is used

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