30
Towards Explainable and Stable Prediction Peng Cui Tsinghua University
Towards Explainable and Stable Prediction
Peng Cui
Tsinghua University
Right time to consider Risk of Today’s AI
2
Human
Healthcare Law
Transportation Fintech
Black-box Model
Risk of Today’s AI
Slide from DARPA
Human in the loopUnexplainable
Medical Military Finance
Risk of Today’s AI Algorithms
5
Yes
Maybe
No
Risk of Today’s AI Algorithms
• Cancer survival rate prediction
6
Training Data
Predictive Model
Testing Data
City Hospital
University Hospital
Features:
• Body status
• Income
• Treatments
• Medications
Higher income, higher survival rate.
City Hospital
Survival rate is not so correlated with income.
Risk of Today’s AI Algorithms
• It’s the fault of Data!
• Our models are designed under the IID hypothesis.
7
Why they fail?
Training Distribution
Test Distribution
Model
8
Correlation: 0.95
P-value: e-10
Statistical Support
• Comes down to the Model
9
Research Problems
ModelDistribution 1
Distribution 1
Distribution 2
Distribution 3
Distribution n
…
Accuracy 1
Accuracy 2
Accuracy 3
Accuracy n
…
I.I.D. Learning
Transfer Learning
VAR (Acc)Stable
Prediction
A fundamental thought
10
Causality
ExplainabilityStability
Causality for Explainability
11
• Prediction / Classification
• 𝑋: vector of features; 𝑌 = 0,1
• Suppose 𝑋 = {𝑆, 𝑉}, and 𝑌 = 𝑓 𝑆 + 𝜀
• 𝑆: set of stable (causal) features
• 𝑉: set of non-causal features
• 𝑃(𝑌|𝑆) is stable, but 𝑃(𝑌|𝑉) is not stable
• Y and V is NOT independent
• Some 𝒗 ⊆ 𝑽 would be learned as
important predictors
Causality for Stability
12
Spurious Correlation !
Towards stable prediction
• Discard spurious correlation and embrace causality.
13
X
T Y
Typical Causal Framework
Estimate the causal effect oftreatment T on output Yunder the confounder X
(A/B Testing)
X T YEstimate the correlation effect
of variable T and output Ywithout evaluating the
relationships between X and T.Typical Correlation Framework
Causal Inference by Absolute Matching
14
X
T Y
Typical Causal Framework
Analogy of A/B Testing
Given a feature T
Find out the sample pairs that one contains
T while the other don’t, but they are similar
in all other features.
Calculate the difference of Y distribution in
treated and controlled groups. (correlation
between T and Y)
The requirement is too strong and we can hardly find satisfied groups
of samples.
Causal Inference by Confounder Balancing
15
X
T Y
Typical Causal Framework
Too many parameters. For N samples and K features, we need to
learn K*N weights. Not learning-friendly.
Analogy of A/B Testing
Given a feature T
Assign different weights to samples so that
the samples with T and the samples without
T have similar distributions in X
Calculate the difference of Y distribution in
treated and controlled groups. (correlation
between T and Y)
Global Balancing: bridging causality and prediction
16
X
T Y
Typical Causal Framework
Reduce the parameter number from K*N to N.
Analogy of A/B Testing
Given ANY feature T
Assign different weights to samples so that the
samples with T and the samples without T have
similar distributions in X
Calculate the difference of Y distribution in
treated and controlled groups. (correlation
between T and Y)
Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Li, Bo Li. Stable Prediction across Unknown Environments. KDD, 2018.
Theoretical Guarantee
17
Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Li, Bo Li. Stable Prediction across Unknown Environments. KDD, 2018.
0
Causal Regularizer
18
All featuresexcluding
treatment j
Set feature j as treatment variable
SampleWeights
Indicator oftreatment
status
Zheyan Shen, Peng Cui, Kun Kuang, Bo Li. Causally Regularized Learning on Data with Agnostic Bias. ACM MM, 2018.
Causally Regularized Logistic Regression
19
Samplereweightedlogistic loss
CausalContribution
Zheyan Shen, Peng Cui, Kun Kuang, Bo Li. Causally Regularized Learning on Data with Agnostic Bias. ACM MM, 2018.
From Shallow to Deep - DGBR
20
Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Li, Bo Li. Stable Prediction across Unknown Environments. KDD, 2018.
From Autoencoder to CNN - CNBB
21
Yue He, Zheyan Shen, Peng Cui. Towards Non-I.I.D. Image Classification: A Dataset and Baselines. (under review)
NICO Dataset (released)
• 19 categories, 10 contexts for each category, ~1300 images for each category
22
23
NICO Dataset (released)
Experimental Result - insights
Experimental Result – Stability
25
Traditional regression models are very sensitive to non-iid
setting. But our model performs stably.
Experimental Result - insights
26
Experiment 2 – online advertising
• Environments generating:
• Separate the whole dataset into 4 environments by users’ age, including
𝐴𝑔𝑒 ∈ [20,30), 𝐴𝑔𝑒 ∈ [30,40), 𝐴𝑔𝑒 ∈ [40,50), and 𝐴𝑔𝑒 ∈ [50,100).
27
Conclusions
• Predictive modeling is not only about Accuracy.
• Stability and Explainability are critical for us to trust a predictive
model.
• Causality has been demonstrated to be useful in stable prediction.
• How to marry causality with predictive modeling effectively and
efficiently is still an open problem.
28
30
Hao Zou, Kun Kuang, Boqi Chen, Peng Cui, Peixuan Chen. Focused Context Balancing for Robust
Offline Policy Evaluation. KDD, 2019.
Kun Kuang, Peng Cui, Susan Athey, Ruoxuan Li, Bo Li. Stable Prediction across Unknown
Environments. KDD, 2018.
Zheyan Shen, Peng Cui, Kun Kuang, Bo Li. Causally Regularized Learning on Data with Agnostic
Bias. ACM Multimedia, 2018.
Kun Kuang, Peng Cui, Bo Li, Shiqiang Yang. Estimating Treatment Effect in the Wild via
Differentiated Confounder Balancing. KDD, 2017.
Kun Kuang, Peng Cui, Bo Li, Shiqiang Yang. Treatment Effect Estimation with Data-Driven Variable
Decomposition. AAAI, 2017.
Yue He, Zheyan Shen, Peng Cui. Towards Non-I.I.D. Image Classification: A Dataset and
Baselines. (under review)
Zheyan Shen, Peng Cui, Tong Zhang. Stable Learning of Linear Models via Sample Reweighting.
(under review)
Reference
