Part 4 — Population, Causality, Fairness

Part 4 — Population, Causality, Fairness#

⚠️ AI-synthesized; not fully reviewed by course staff. Treat as a study aid; released slides, notebooks, and lecture recordings are authoritative.

Lectures 23-24

What this part is about#

Part 4 lifts the lens beyond “what happens in a single hospital’s EHR / imaging system” to the population scale, and then formalizes a warning that has been threading through the whole course since L1: prediction and intervention are different questions, and conflating them is one of the most consequential mistakes in clinical AI.

Two lectures:

  • L23 — Population health & survival analysis broadens the data lens to designed surveys (NHANES), cancer registries (SEER), biobanks (UK Biobank, All of Us), and clinical trials, then introduces survival analysis as the principled framework for time-to-event outcomes with censoring (Kaplan-Meier, Cox, DeepSurv, DeepHit).

  • L24 — Causality & fairness finally writes down the prediction-vs.-intervention distinction with causal DAGs, propensity scores, and fairness criteria. The Chouldechova / Kleinberg impossibility says that two natural-sounding fairness metrics (sufficiency vs. separation) cannot both be satisfied when base rates differ — which they almost always do in health. Obermeyer et al. (2019) is the canonical case study: a fair-looking algorithm that under-served Black patients because the label (cost) was a biased proxy for the target (need).

Goals for this part#

  • Map a clinical or research question to the right data source (registry, survey, biobank, trial, EHR), and identify the selection biases each carries.

  • Read a Kaplan-Meier curve; interpret a Cox forest plot; recognize when binary classification at a fixed horizon is hiding survival structure.

  • Read a causal DAG; distinguish chain / fork / collider; reason about when conditioning helps and when it hurts.

  • Apply propensity-score weighting / matching / stratification in observational comparisons.

  • State and apply the canonical fairness criteria (sufficiency vs. separation, equalized odds, calibration-within-group); explain why they’re mutually incompatible at unequal base rates.

  • Audit an ML pipeline for label bias (à la Obermeyer): does the label measure what we actually care about, or a proxy that disadvantages some group?

Key takeaways for this part#

  • The right data source depends on the question. No source is universally best.

  • Censoring and competing risks are structural, not accidental. Survival analysis is what handles them principally.

  • p(Y|X) ≠ p(Y|do(X)) in general. Prediction models do not, by themselves, license intervention claims.

  • Sufficiency and separation are different fairness families; they are mathematically incompatible under unequal base rates.

  • Label bias is invisible to fairness criteria that only look at scores. Always ask: what does the label measure?

Lectures in this part#

External resources for this part#

Survival analysis (L23)

  • Kaplan & Meier, “Nonparametric estimation from incomplete observations,” JASA 53, 1958. Original; surprisingly readable.

  • Cox, “Regression Models and Life-Tables,” JRSS B 34, 1972.

  • Katzman et al., “DeepSurv,” BMC Med Res Methodol 18, 2018.

  • Lee et al., “DeepHit,” AAAI 2018.

  • Bycroft et al., “The UK Biobank resource with deep phenotyping and genomic data,” Nature 562, 2018.

  • NHANES analytic guidelines (CDC) and SEER documentation (NCI).

  • Coursera, Survival Analysis in R for Public Health (Imperial College).

Causality & fairness (L24)

  • Pearl, Glymour & Jewell, Causal Inference in Statistics: A Primer, 2016. Readable intro; start here.

  • Hernán & Robins, Causal Inference: What If (free PDF online). The applied-epidemiology lens.

  • Pearl, Causality (2nd ed.), 2009. The full book; harder.

  • Obermeyer, Powers, Vogeli & Mullainathan, “Dissecting racial bias in an algorithm used to manage the health of populations,” Science 366, 2019. Required reading. Twice.

  • Chouldechova, “Fair prediction with disparate impact: A study of bias in recidivism prediction instruments,” Big Data 5, 2017.

  • Kleinberg, Mullainathan, Raghavan, “Inherent Trade-Offs in the Fair Determination of Risk Scores,” ITCS 2017.

  • Rajkomar et al., “Ensuring Fairness in Machine Learning to Advance Health Equity,” Ann Intern Med 169, 2018.

  • Coursera, A Crash Course in Causality (Roy, U. Pittsburgh) — free, hands-on companion.