The document discusses the differences between prediction and causality. It notes that while correlation is good for prediction, correlation does not necessarily imply causation. Causal discovery requires methods beyond simply observing correlations as determining the fundamental causes of phenomena is often difficult. Deep learning methods that rely solely on correlations learned from data are just a form of curve fitting rather than a way to understand causal relationships.

1. PREDICTION
VS
CAUSALITY

2. JÁNOS DIVÉNYI
lead data scientist @Emarsys
PhD candidate in economics @CEU
@divenyijanos

4. IGNAZ
SEMMELWEIS

5. 0
5
10
15
20
1841 1842 1843 1844 1845 1846
Mortalityrate(%)
First Clinic
Second Clinic

6. Pr(Y|X) à xgboost
Y: mother dies
X: temperature, midwife/doctor, etc.

7. Pr(Y|X) à xgboost
Y: mother dies
X: temperature, midwife/doctor, etc.

8. Pr(Y|X) à xgboost
Y: mother dies
X: temperature, midwife/doctor, etc.
ACCURACY
50%+

9. SAVIOR OF
MOTHERS

10. booking.com

11. CORRELATION DOES NOT
IMPLY CAUSATION

12. xkcd.com/552

13. PREDICTION VS CAUSALITY

14. PREDICTION VS CAUSALITY

15. PREDICTION VS CAUSALITY

17. qz.com

18. qz.com

19. DEEP LEARNING IS JUST
CURVE FITTING
DEEP LEARNING IS JUST
CURVE FITTING

20. JUDEA PEARL
Turing Award
quantamagazine.org
”DEEP
LEARNING
IS JUST
CURVE
FITTING”

23. Buying
probability

24. Buying
probability
Reaction
to incentives

25. Buying
probability
Reaction
to incentives

28. CORRELATION IS GOOD FOR PREDICTION
CORRELATION DOES NOT MEAN CAUSALITY
CAUSAL DISCOVERY IS OFTEN CUMBERSOME

29. CORRELATION IS GOOD FOR PREDICTION
CORRELATION DOES NOT MEAN CAUSALITY
CAUSAL DISCOVERY IS OFTEN CUMBERSOME

30. CORRELATION IS GOOD FOR PREDICTION
CORRELATION DOES NOT MEAN CAUSALITY
CAUSAL DISCOVERY IS OFTEN CUMBERSOME

31. THANKS
@divenyijanos