1. 1 Richard Scheines Carnegie Mellon University Causal Graphical Models II: Applications with Search

2. 2 1.Foreign Investment 2.Welfare Reform 3.Online Learning 4.Charitable Giving 5.Stress & Prayer 6.Test Anxiety 7.Causal Connectivity among Brain Regions Case Studies

3. 3 1.Exceedingly simple 2.Background theory weak 3.Claim: –Not: search output is true –Is: search adds value Case Studies

4. 4 Case Study 1: Foreign Investment Does Foreign Investment in 3 rd World Countries cause Political Repression? Timberlake, M. and Williams, K. (1984). Dependence, political exclusion, and government repression: Some cross-national evidence. American Sociological Review 49, 141-146. N = 72 POdegree of political exclusivity CVlack of civil liberties ENenergy consumption per capita (economic development) FIlevel of foreign investment

5. 5 Correlations po fi en fi -.175 en -.480 0.330 cv 0.868 -.391 -.430 Case Study 1: Foreign Investment

6. 6 Regression Results po =.227*fi -.176*en +.880*cv SE (.058) (.059) (.060) t 3.941 -2.99 14.6 Interpretation: foreign investment increases political repression Case Study 1: Foreign Investment

7. Alternatives Case Study 1: Foreign Investment There is no model with testable constraints (df > 0) in which FI has a positive effect on PO that is not rejected by the data.

8. 8 Aurora Jackson, Richard Scheines Single Mothers’ Self-Efficacy, Parenting in the Home Environment, and Children’s Development in a Two-Wave Study (Social Work Research, 29, 1, 7-20) Case Study 2: Welfare Reform

9. 9 Two-Wave Longitudinal Study Longitudinal Data oTime 1: 1996-97 (N = 188) oTime 2: 1998-99 (N = 178) Single black mothers in NYC Current and former welfare recipients With a child who was 3 – 5 at time 1, and 6 to 8 at time 2 Case Study 2: Welfare Reform

10. 10 Constructs/Scales/Measures Employment Status Perceived Self-efficacy Depressive Symptoms Quality of Mother/Father Relationship Father/Child Contact Quality of Home Environment Behavior Problems Cognitive Development Case Study 2: Welfare Reform

11. 11 Background Knowledge Tier 1: Employment Status Tier 2: Depression Self-efficacy Mother/Father Relationship Father/Child Contact Mother’s Parenting/HOME Tier 3: Negative Behaviors Cognitive Development Over 22 million path models consistent with these constraints Case Study 2: Welfare Reform

12. 12 Tetrad Equivalence Class Conceptual Model  2 = 22.3, df = 20, p =.32  2 = 18.87, df = 19, p =.46 Case Study 2: Welfare Reform

13. 13 Tetrad Conceptual Model Points of Agreement: Mother’s Self-Efficacy mediates the effect of Employment on all other variables. Home environment mediates the effect of all other factors on outcomes: Cog. Develop and Prob. Behaviors Points of Disagreement: Depression key cause vs. only an effect Case Study 2: Welfare Reform

14. 14 Online Course in Causal & Statistical Reasoning Case Study 3: Online Courseware

15. 15 Variables  Pre-test (%)  Print-outs (% modules printed)  Quiz Scores (avg. %)  Voluntary Exercises (% completed)  Final Exam (%)  9 other variables Case Study 3: Online Courseware Tier 1 Tier 2 Tier 3

16. 16 Printing and Voluntary Comprehension Checks: 2002 --> 2003 2002 2003 Case Study 3: Online Courseware

17. 17 Variables  Tangibility/Concreteness (Exp manipulation)  Imaginability (likert 1-7)  Impact (avg. of 2 likerts)  Sympathy (likert)  Donation ($) Case Study 4: Charitable Giving Cryder & Loewenstein (in prep)

18. 18 Theoretical Model Case Study 4: Charitable Giving study 1 (N= 94) df = 5,  2 = 52.0, p= 0.0000

19. 19 GES Outputs Case Study 4: Charitable Giving study 1: df = 5,  2 = 5.88, p= 0.32 study 1: df = 5,  2 = 3.99, p= 0.55

20. 20 Theoretical Model Case Study 4: Charitable Giving study 2 (N= 115) df = 5,  2 = 62.6, p= 0.0000 study 2: df = 5,  2 = 8.23, p= 0.14 study 2: df = 5,  2 = 7.48, p= 0.18

21. 21 Build Pure Clusters Output - provably reliable (pointwise consistent): Equivalence class of measurement models over a pure subset of measures True Model Output

22. 22 Build Pure Clusters Qualitative Assumptions 1.Two types of nodes: measured (M) and latent (L) 2.M L (measured don’t cause latents) 3.Each m  M measures (is a direct effect of) at least one l  L 4.No cycles involving M Quantitative Assumptions: 1.Each m  M is a linear function of its parents plus noise 2.P(L) has second moments, positive variances, and no deterministic relations

23. 23 Case Study 5: Stress, Depression, and Religion MSW Students (N = 127) 61 - item survey (Likert Scale) Stress: St 1 - St 21 Depression: D 1 - D 20 Religious Coping: C 1 - C 20 p = 0.00 Specified Model

24. 24 Build Pure Clusters Case Study 5: Stress, Depression, and Religion

25. 25 Assume Stress temporally prior: MIMbuild to find Latent Structure: p = 0.28 Case Study 5: Stress, Depression, and Religion

26. 26 Case Study 6: Test Anxiety Bartholomew and Knott (1999), Latent variable models and factor analysis 12th Grade Males in British Columbia (N = 335) 20 - item survey (Likert Scale items): X 1 - X 20 : Exploratory Factor Analysis:

27. 27 Build Pure Clusters : Case Study 6: Test Anxiety

28. 28 Build Pure Clusters: p-value = 0.00p-value = 0.47 Exploratory Factor Analysis: Case Study 6: Test Anxiety

29. 29 MIMbuild p =.43Uninformative Scales: No Independencies or Conditional Independencies Case Study 6: Test Anxiety

30. 30 Goals: –Identify relatively BIG brain regions (ROIs). –Figure out how they influence one another, with what timing sequences, in producing behaviors of interest. –Figure out individual differences. Case Study 7: fMRI  Brain Connectivity

31. Experiment: (Xue and Poldrack, unpublished) –13 right handed subjects –On each trial, subject judged whether visual stimuli rhymed or not –8 pairs of words/nonwords presented for 2.5 seconds each in eight 20 second blocks, separated by 20 seconds of visual fixation –TR = 2000 milliseconds –160 time points. 31 Case Study 7: fMRI

32. 32 Problems: –Criteria for identifying ROIs –Individuals differ Brain ROIs Parameter values –Brain processing is cyclic –Time: Varying time delays of neuron  ROI BOLD response Time series sampling rate vs. processing rate –Search Space 11 ROIs – 3 23 DAGs Case Study 7: fMRI  Brain Connectivity

33. ROI Construction Mean of signal intensity among voxels in a cluster at a time 1 st or....4 th principal component Average of top X% variance Maximum variance voxel. Eyeballs Etc., etc Case Study 7: fMRI

34. Example ROIs Case Study 7: fMRI

35. 35 –Individuals differ Brain ROIs Parameter values Case Study 7: fMRI  Brain Connectivity –Assume same qualitiative causal structure different quantitative causal structure (mixed effects) –iMAGES search Apply GES to each subject, 1 step Take step = max(avg. BIC score) to each search Repeat

36. Time Problem 1 fMRI recordings at time intervals can be analyzed as a collection of independent cases. Or, they can be analyzed as an auto-regressive time series. Which is better? –No general answer. –But if you think the neural activities measured at time t influence the measurements at time t+1 then the data should be treated as a lag 1 auto-regressive time series. –But then Granger causality isn’t a consistent estimator of causal relations. 36 Case Study 7: fMRI

37. Granger Causality Corrected Causal processes faster than the sampling rate: X t X t+1 X Y t Y t+1 Y Z t Z t+1 Z Regress on t variables Apply GES to the RESIDUALS of the regression (Demiralp, Hoover) NO False path Case Study 7: fMRI

38. Time Problem 2 Varying time delays : neurons  BOLD responses 38 Case Study 7: fMRI Try all time shifts of one or two units over all subsets of 3 vars, choose shift that leads to best likelihoods

39. Lag 0 resultLag 1 result. 39

40. Simulation Studies: 11 ROIs, each consisting of 50 simulated neurons: Neuron output spikes simulated by thresholding a tanh function of the sum of neuron inputs. Excitatory feedback Random subset of neurons in one ROI input to random subset of neurons in an “effectively connected ROI” Measured variables = BOLD function of sum of ROI neurons + Gaussian error with variance = error variances of empirical measured variables in the X/P experiment. 40 Case Study 7: fMRI

41. Repeat 10 times: –Randomly generate a graphical structure with 11 nodes and 11 (feedforward) directed edges –Randomly select a subset of simulated ROIs. –Generate data –Randomly shift 0 to 3 variables one or 2 time steps forward. –Apply the iMAGES method with 0 lag and 1 lag, with backshifting. Tabulate the errors. 41 Simulate the Xue/Poldrack Experiment Time Series: Case Study 7: fMRI

42. Simulation Results 0 Lag: Average number of false positive edges: 0.7 Average number of mis-directed edges: 1.6 1 Lag Residuals: Average number of false positive edges: 1.2 Average number of mis-directed edges: 1.8 42 Case Study 6: fMRI

43. 43 Economics  Bessler, Pork Prices  Hoover, multiple  Cryder & Loewenstein, Charitable Giving Other Cases Educational Research  Easterday, Bias & Recall  Laski, Numerical coding Climate Research  Glymour, Chu,, Teleconnections Biology  Shipley,  SGS, Spartina Grass Neuroscience  Glymour & Ramsey, fMRI Epidemiology  Scheines, Lead & IQ

44. 44 Straw Men! Model Search ignores theory Model Search hides assumptions Model Search needs more assumptions than standard statistical models

45. 45 References Biology Chu, Tianjaio, Glymour C., Scheines, R., & Spirtes, P, (2002). A Statistical Problem for Inference to Regulatory Structure from Associations of Gene Expression Measurement with Microarrays. Bioinformatics, 19: 1147-1152. Shipley, B. Exploring hypothesis space: examples from organismal biology. Computation, Causation and Discovery. C. Glymour and G. Cooper. Cambridge, MA, MIT Press. Shipley, B. (1995). Structured interspecific determinants of specific leaf area in 34 species of herbaceous angeosperms. Functional Ecology 9. General Spirtes, P., Glymour, C., Scheines, R. (2000). Causation, Prediction, and Search, 2 nd Edition, MIT Press. Pearl, J. (2000). Causation : Models of Reasoning and Inference, Cambridge University Press.

46. 46 References Scheines, R. (2000). Estimating Latent Causal Influences: TETRAD III Variable Selection and Bayesian Parameter Estimation: the effect of Lead on IQ, Handbook of Data Mining, Pat Hayes, editor, Oxford University Press. Jackson, A., and Scheines, R., (2005). Single Mothers' Self-Efficacy, Parenting in the Home Environment, and Children's Development in a Two-Wave Study, Social Work Research, 29, 1, pp. 7-20. Timberlake, M. and Williams, K. (1984). Dependence, political exclusion, and government repression: Some cross-national evidence. American Sociological Review 49, 141-146.

47. 47 References Economics Akleman, Derya G., David A. Bessler, and Diana M. Burton. (1999). ‘Modeling corn exports and exchange rates with directed graphs and statistical loss functions’, in Clark Glymour and Gregory F. Cooper (eds) Computation, Causation, and Discovery, American Association for Artificial Intelligence, Menlo Park, CA and MIT Press, Cambridge, MA, pp. 497-520. Awokuse, T. O. (2005) “Export-led Growth and the Japanese Economy: Evidence from VAR and Directed Acyclical Graphs,” Applied Economics Letters 12(14), 849-858. Bessler, David A. and N. Loper. (2001) “Economic Development: Evidence from Directed Acyclical Graphs” Manchester School 69(4), 457-476. Bessler, David A. and Seongpyo Lee. (2002). ‘Money and prices: U.S. data 1869-1914 (a study with directed graphs)’, Empirical Economics, Vol. 27, pp. 427-46. Demiralp, Selva and Kevin D. Hoover. (2003) !Searching for the Causal Structure of a Vector Autoregression," Oxford Bulletin of Economics and Statistics 65(supplement), pp. 745-767. Haigh, M.S., N.K. Nomikos, and D.A. Bessler (2004) “Integration and Causality in International Freight Markets: Modeling with Error Correction and Directed Acyclical Graphs,” Southern Economic Journal 71(1), 145-162. Sheffrin, Steven M. and Robert K. Triest. (1998). ‘A new approach to causality and economic growth’, unpublished typescript, University of California, Davis.

48. 48 References Economics Swanson, Norman R. and Clive W.J. Granger. (1997). ‘Impulse response functions based on a causal approach to residual orthogonalization in vector autoregressions’, Journal of the American Statistical Association, Vol. 92, pp. 357-67. Demiralp, S., Hoover, K., & Perez, S. A Bootstrap Method for Identifying and Evaluating a Structural Vector Autoregression Oxford Bulletin of Economics and Statistics, 2008, 70, (4), 509- 533 - Searching for the Causal Structure of a Vector Autoregression Oxford Bulletin of Economics and Statistics, 2003, 65, (s1), 745-767 Kevin D. Hoover, Selva Demiralp, Stephen J. Perez, Empirical Identification of the Vector Autoregression: The Causes and Effects of U.S. M2*, This paper was written to present at the Conference in Honour of David F. Hendry at Oxford University, 2325 August 2007. Selva Demiralp and Kevin D. Hoover, Searching for the Causal Structure of a Vector Autoregression, OXFORD BULLETIN OF ECONOMICS AND STATISTICS, 65, SUPPLEMENT (2003) 0305-9049 A. Moneta, and P. Spirtes “Graphical Models for the Identification of Causal Structures in Multivariate Time Series Model”, Proceedings of the 2006 Joint Conference on Information Sciences, JCIS 2006, Kaohsiung, Taiwan, ROC, October 8-11,2006, Atlantis Press, 2006.

49. 49 Extra

50. Lead and IQ: Variable Selection Final Variables (Needleman) -leadbaby teeth -fabfather’s age -mabmother’s age -nlbnumber of live births -medmother’s education -piqparent’s IQ -ciqchild’s IQ

51. Needleman Regression - standardized coefficient - (t-ratios in parentheses) - p-value for significance ciq = -.143 lead -.204 fab -.159 nlb +.219 med +.237 mab +.247 piq (2.32) (1.79) (2.30) (3.08) (1.97) (3.87) 0.02 0.09 0.02 <0.01 0.05 <0.01 All variables significant at.1 R 2 =.271

52. TETRAD Variable Selection Tetrad mab _||_ ciq fab _||_ ciq nlb _||_ ciq | med Regression mab _||_ ciq | { lead, med, piq, nlb fab} fab _||_ ciq | { lead, med, piq, nlb mab} nlb _||_ ciq | { lead, med, piq, mab, fab}

53. Regressions - standardized coefficient - (t-ratios in parentheses) - p-value for significance Needleman (R 2 =.271) ciq = -.143 lead -.204 fab -.159 nlb +.219 med +.237 mab +.247 piq (2.32) (1.79) (2.30) (3.08) (1.97) (3.87) 0.02 0.09 0.02 <0.01 0.05 <0.01 TETRAD (R 2 =.243) ciq = -.177 lead +.251 med +.253 piq (2.89) (3.50) (3.59) <0.01 <0.01 <0.01

54. Measurement Error Measured regressor variables are proxies that involve measurement error Errors-in-all-variables model for Lead’s influence on IQ - underidentified Strategies: Sensitivity Analysis Bayesian Analysis

55. Prior over Measurement Error Proportion of Variance from Measurement Error Measured Lead Mean =.2,SD =.1 Parent’s IQMean =.3,SD =.15 Mother’s Education Mean =.3,SD =.15 Prior Otherwise uninformative

56. Posterior Zero Robust over similar priors

57. Using Needleman’s Covariates With similar prior, the marginal posterior: Very Sensitive to Prior Over Regressors TETRAD eliminated Zero