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Samantha Kleinberg, Assistant Professor of Computer Science, Stevens Institute of Technology at MLconf NYC - 4/15/16

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Causal Inference and Explanation to Improve Human Health: Massive amounts of medical data such as from electronic health records and body-worn sensors are being collected and mined by researchers, but translating findings into actionable knowledge remains difficult. The first challenge is finding causes, rather than correlations, when the data are highly noisy and often missing. The second is using these to explain specific cases, such as why an individual’s blood glucose is raised. In this talk I discuss new methods for both causal inference and explanation, and show how these could be used to provide individualized feedback to patients.

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Samantha Kleinberg, Assistant Professor of Computer Science, Stevens Institute of Technology at MLconf NYC - 4/15/16

  1. 1. Causal Inference and Explanation to Improve Human Health Samantha Kleinberg Stevens Institute of Technology
  2. 2. Can we learn.. risk factors for heart failure causes of secondary brain injury what leads to an individual’s hypoglycemic episodes from observing people?
  3. 3. Why worry about causality? • Robust predictions • Coherent explanations • Actionable information
  4. 4. Why observational data? • Experiments often infeasible, unethical, or too expensive • Routinely collected in many situations • Electronic health records in hospitals • ICU data streams • Body-worn sensors and mobile devices
  5. 5. This talk • We can make some progress in getting causes from medical data • But should worry about missing data and nonstationarity • Explanation can be automated (sometimes)
  6. 6. Logic-based causal inference • Complex, temporal relationships • Assess average difference cause makes to probability of effect Kleinberg, S. (2012) Causality, Probability, and Time. Cambridge University Press. Kleinberg, S. (2015) Why: A Guide to Finding and Using Causes. O’Reilly Media
  7. 7. • Main idea: looking for better explanations for the effect • Inferring timing • Instead of accepting/rejecting hypotheses, refine them from data using greedy search • Can start by testing relationships between all variables and CHF in 1-2 weeks, and ultimately infer "high AST leads to CHF in 4-10 days”
  8. 8. Application: understanding stroke recovery Massive amounts of data collected in ICU (>100,000 measurements per person) How do patients change over time?
  9. 9. NICU dataset • 98 patients with subarachnoid hemorrhage • Monitoring included • Depth and surface EEG • Microdialysis • Physiologic measurements (no data on procedures)
  10. 10. Lots of data, but lots of missing data • Device malfunctions • Device connected to perform a procedure • Different monitors started at different times • Different recording frequencies TW% BrT CI CO2EX CPP CVP ELWI GEDI GLU GLU2panel HR ICP 0.56001433 0.55845172 0.45439822 0.77027571 0.89076666 0.80927443 0.47893916 0.47893916 0.07274971 0.07943021 0.98646971 0.92582606 0.69029286 0.71585128 0.67012404 0.80326783 0.91217418 0.84294797 0.56494303 0.56494303 0.13405794 0.03836355 0.98382787 0.94543471 56.0014325 55.845172 45.4398223 77.0275708 89.0766656 80.9274429 47.893916 47.893916 7.27497093 7.94302149 98.6469713 92.5826062 69.0292858 71.5851282 67.0124038 80.3267833 91.2174176 84.294797 56.4943028 56.4943028 13.4057939 3.83635458 98.3827871 94.5434715 0D 20D 40D 60D 80D 100D TW%D BrTD CID CO2EXD CPPD CVPD ELWID GEDID GLUD GLU2panelD HRD ICPD LACD LGRD LPRD MAPD MVD PEEPD PGRD PYRD PbtO2D RRD SPO2%D SVVD SvO2D TMPD rCBFD %"data"recorded"per" variable" 0296hrsD 962168hrsD
  11. 11. And… • All variables may be missing at once (if measured by single device) • Can’t use imputation methods that assume some present values • Missing values depend on variable (NMAR) + other variables (MAR) • e.g. BG depends on itself, as well as insulin • Variables are correlated across time
  12. 12. Imputation w/lagged correlations S. A. Rahman, Y. Huang, J. Claassen, N. Heintzman, and S. Kleinberg. Combining Fourier and Lagged k-Nearest Neighbor Imputation for Biomedical Time Series Data. Journal of Biomedical Informatics (2015) https://github.com/kleinberg-lab/FLK-NN
  13. 13. Evaluation
  14. 14. Regulation changes over time 0-96hrs 96-168 hrs Claassen J, Rahman SA, Huang Y, Frey H, Schmidt M, Albers D, Falo CM, Park S, Agarwal S, Connolly ES, Kleinberg S (2015) Causal structure of brain physiology after brain injury. PLoS ONE.
  15. 15. Explanation • Why is an individual’s brain swelling at a particular time? • What caused a specific stock market crash? • Who is at fault for a car accident?
  16. 16. • Inference finds type-level relationships
 
 
 
 • Explanation tells us why specific events happened
 
 
 
 heart failure uncontrolled diabetes thyroid disfunction heart failure uncontrolled diabetes thyroid disfunction
  17. 17. • Goals for explanation • Find causes of specific events automatically (no human in the loop) • Find causes of when, whether and how events occur • Approach: simulation to answer counterfactual queries C. Merck and S. Kleinberg. Causal explanation under indeterminism: A sampling approach. AAAI, 2016. https://github.com/kleinberg-lab/stoch_cf
  18. 18. Counterfactual vs. Actual Distributions • P(•|¬A) is the counterfactual distribution of A
 
 • P(•|A) is the actual distribution of A

  19. 19. Three Types of Explanation B because of A iff
 P (B|A) >> P (B|¬A) B hastened by A iff
 E[tB |A] << E[tB |¬A] B intensified by A iff
 E[mB |A] >> E[mB |¬A] 
 B despite A iff
 P (B|A) << P (B|¬A) B delayed by A iff
 E[tB |A] >> E[tB |¬A] B attenuated by A iff
 E[mB |A] << E[mB |¬A] 
 tB = time of B occurring mB = intensity (manner) of B occurring probability:
 timing:
 intensity:
  20. 20. Causal Chain with Billiard Balls
  21. 21. Hastening • 6->8, then 8->P • but 7->8 is a more reliable backup • probability raising finds
 “8->P despite 6->8” • but by analyzing timing we find
 “6->8 hastened 8->P”
  22. 22. Diabetes Simulation • Run or Lunch alone would not have caused Hypoglycemia
 (see counterfactual dists) • Yet together they explain the Hypoglycemia
 (see actual distribution) • We see beyond the most recent event (Lunch) • We can measure quantitative strength of effect in mg/dL:
 E[ glu | R] - E[ glu | ¬R]
  23. 23. Looking forward: personalized and pervasive sensing • Can we develop a fitbit for nutrition? • Find effect of food on glucose, causes of overeating
  24. 24. Key open problems • Data with uncertain timings • Finding the “right” variables • Explanation beyond models
  25. 25. Thanks! Teams @CUMC, Stevens NSF, NIH, JSMF And my group is hiring!

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