From prediction to causation: Causal inference in online systems
Predictive modeling and machine learning has been widely successful: such models can suggest movies, songs or games to try out, identify people to target ads for, predict customer churn, detect fraud, identify health risks and so on. However, predictive models are not well-equipped to answer questions about cause and effect, which form the logical next step of action after establishing correlational models: what should one do next to improve key metrics or goals? For example, what is the effect of a recommender algorithm? if a product recommendation system is changed or removed, what will be the effect on people's purchases? If having more friends is correlated with higher activity on a social network, would encouraging users to add more friends increase their activity? This tutorial won't give all the answers, but will provide a principled way to reason about causal effects and estimate them. In the first half of the tutorial, I will present an overview of counterfactual reasoning and common methods for causal inference. The second half is hands-on: a practical example of estimating the causal impact of a recommender system, starting from simple methods to more complex methods, with the side-goal of appreciating and learning from common pitfalls in causal inference. Code and resources for the tutorial available at: https://github.com/amit-sharma/causal-inference-tutorial/ Amit Sharma should we focus on encouraging Xbox users to add more friends? In the first half of the tutorial, I will present an overview of counterfactual reasoning and common methods for causal inference. In the second half, participants will work through a practical example of estimating the causal impact of a recommender system, starting from simple methods to more complex methods, with the side-goal of appreciating and learning from common pitfalls in causal inference.
Recomendados
Recomendados
Mais conteúdo relacionado
Destaque
Destaque (9)
Mais de Amit Sharma
Mais de Amit Sharma (16)
Último
Último (20)
From prediction to causation: Causal inference in online systems
- 2. 2
- 3. 3
- 4. 4
- 5. 5
- 6. Use these correlations to make a predictive model. Future Activity -> f(number of friends, logins in past month) 6
- 7. 7
- 8. 8
- 9. 9
- 10. 10
- 11. 11
- 12. 12
- 13. 13
- 14. 14
- 15. 15
- 16. 16
- 17. 17
- 18. 18
- 19. 19 Old Algorithm (A) New Algorithm (B) 50/1000 (5%) 54/1000 (5.4%)
- 20. 20 Old Algorithm (A) New Algorithm (B) 10/400 (2.5%) 4/200 (2%) Old Algorithm (A) New Algorithm (B) 40/600 (6.6%) 50/800 (6.2%) 0 2 4 6 8 Low-activity High-activity CTR
- 21. Is Algorithm A better? Old algorithm (A) New Algorithm (B) CTR for Low- Activity users 10/400 (2.5%) 4/200 (2%) CTR for High- Activity users 40/600 (6.6%) 50/800 (6.2%) Total CTR 50/1000 (5%) 54/1000 (5.4%) 21
- 22. 22
- 23. Average comment length decreases over time. 23 But for each yearly cohort of users, comment length increases over time.
- 24. 24
- 25. 25
- 26. 26
- 29. 29
- 30. 30
- 31. 31
- 32. 32
- 33. 33
- 34. 34
- 35. 35
- 36. 36
- 37. 37
- 38. 38
- 39. 39
- 40. 40
- 41. 41Dunning (2002), Rosenzweig-Wolpin (2000)
- 42. 42
- 43. 43
- 44. 44
- 45. 45
- 46. 46
- 47. 47
- 48. 48
- 49. 49
- 50. 50
- 51. 51
- 52. 52
- 53. 53
- 54. 54
- 55. 55 Does new Algorithm B increase CTR for recommendations on Windows Store, compared to old algorithm A?
- 56. Does new Algorithm B increase CTR for recommendations on Windows Store, compared to old algorithm A? 56
- 57. 57
- 58. 58
- 59. 59
- 60. 60
- 61. 61
- 62. 62
- 63. 63
- 64. 64
- 65. 65
- 66. 𝑷𝒓𝒐𝒑𝒆𝒏𝒔𝒊𝒕𝒚 𝑁𝑒𝑤𝐴𝑙𝑔𝑜 𝑈𝑠𝑒𝑟𝑖 = 𝑳𝒐𝒈𝒊𝒔𝒕𝒊𝒄(𝑎 𝑐𝑎𝑡1, 𝑎 𝑐𝑎𝑡2, … 𝑎 𝑐𝑎𝑡𝑛) Compare CTR between users with the same propensity score. 66
- 67. 67
- 68. 68
- 70. 70
- 71. 71
- 72. 72
- 74. 74
- 77. 77
- 78. 78
- 79. 79
- 80. 80
- 81. 81
- 82. > nrow(user_app_visits_A) [1] 1,000,000 > length(unique(user_app_visits_A$user_id)) [1] 10,000 > length(unique(user_app_visits_A$product_id)) [1] 990 > length(unique(user_app_visits_A$category)) [1] 10 82
- 83. 83
- 84. 84
- 85. > user_app_visits_B = read.csv("user_app_visits_B.csv") > naive_observational_estimate <- function(user_visits){ # Naive observational estimate # Simply the fraction of visits that resulted in a recommendation click- through. est = summarise(user_visits, naive_estimate=sum(is_rec_visit)/length(is_rec_visit)) return(est) } > naive_observational_estimate(user_app_visits_A) naive_estimate [1] 0.200768 > naive_observational_estimate(user_app_visits_B) naive_estimate [1] 0.226467 85
- 86. 86
- 87. > stratified_by_activity_estimate(user_app_visits_A) Source: local data frame [4 x 2] activity_level stratified_estimate 1 1 0.1248852 2 2 0.1750483 3 3 0.2266394 4 4 0.2763522 > stratified_by_activity_estimate(user_app_visits_B) Source: local data frame [4 x 2] activity_level stratified_estimate 1 1 0.1253469 2 2 0.1753933 3 3 0.2257211 4 4 0.2749867 87
- 88. > stratified_by_category_estimate(user_app_visits_A) Source: local data frame [10 x 2] category stratified_estimate 1 1 0.1758294 2 2 0.2276829 3 3 0.2763157 4 4 0.1239860 5 5 0.1767163 … … … > stratified_by_category_estimate(user_app_visits_B) Source: local data frame [10 x 2] category stratified_estimate 1 1 0.2002127 2 2 0.2517528 3 3 0.3021371 4 4 0.1503150 5 5 0.1999519 … … … 88
- 89. 89
- 90. 90
- 91. 91
- 92. 92
- 93. > naive_observational_estimate(user_app_visits_A) naive_estimate [1] 0.200768 > ranking_discontinuity_estimate(user_app_visits_A) discontinuity_estimate [1] 0.121362 40% of app visits coming from recommendation click- throughs are not causal. Could have happened even without the recommendation system. 93
- 94. 94