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Causal Data Mining Richard Scheines Dept. of Philosophy, Machine Learning, & Human-Computer Interaction Carnegie Mellon.

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Presentation on theme: "Causal Data Mining Richard Scheines Dept. of Philosophy, Machine Learning, & Human-Computer Interaction Carnegie Mellon."— Presentation transcript:

1 Causal Data Mining Richard Scheines Dept. of Philosophy, Machine Learning, & Human-Computer Interaction Carnegie Mellon

2 1. Predictive Data Mining Finding predictive relationships in data –What feature of student behavior predicts learning –Who will default on credit cards –Who will get an “A” in your course –Which HS students will do well at CMU –Do students cluster by “learning style”

3 Causal Data Mining Finding causal relationships in data –What feature of student behavior causes learning –What will happen when we make everyone take a reading quiz before each class –What will happen when we program our tutor to intervene to give hints after an error

4 Predictive Data Mining X1X2X3..XkY M F F N2.812M Data Mining Search Predictive Model Y = f(X1, X2, …Xk)

5 Predictive Data Mining Data Mining Search Predictive Model Y = f(X1, X2, …Xk) Model Classes 1.Simple Regression 2.Locally Weighted Regression 3.Logistic Regression 4.Neural Nets 5.Vector Support Machines 6.Decision Trees 7.Bayes Net 8.Naïve Bayes Classifier 9.Independent Components 10.Clustering 11.Etc.

6 Predictive Data Mining Predictive Model under Constraints Y = f(X1, X2, …Xk), e.g., f  Additive functions Data Mining Search

7 Predictive Data Mining Predictive Model under Constraints Y = f(X1, X2, …Xk), Or Probability Model under Constraints: P(Y | X1, X2, …, Xk), where P  Gaussian, with mean 0 Data Mining Search

8 Predictive Data Mining Decision Tree Search

9 Predictive Data Mining ≠ Causal Data Mining P(Y | X1, X2, …, Xk)  P(Y | X1 set, X2, …, Xk) Conditioning is not the same as intervening Teeth Slides

10 Causal Discovery Statistical Data  Causal Structure Background Knowledge - X 2 before X 3 - no unmeasured common causes Statistical Inference

11 Causal Discovery Software TETRAD IV

12 Full Semester Online Course in Causal & Statistical Reasoning

13 Course is tooled to record certain events:  Logins, page requests, print requests, quiz attempts, quiz scores, voluntary exercises attempted, etc. Each event was associated with attributes:  Time  student-id  Session-id

14 Printing and Voluntary Comprehension Checks: >


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