This document discusses predictive aspects of partial least squares (PLS) modeling. It makes three key points: 1. PLS is considered a "causal-predictive" approach that allows for testing of causal hypotheses through path coefficients and loadings, but its coefficients may be biased. 2. PLS is primarily an explanatory statistical model focused on theory testing, while predictive models specifically aim to accurately predict new observations. 3. The document questions whether PLS truly has predictive aspects, as predictive power is not evaluated in software or research. It provides an example showing a neural network model outperforms PLS in predicting holdout data.

1. What is Predictive
About Partial Least Squares?
Galit Shmueli Otto Koppius
UMD Erasmus

2. Barclay, Higgins & Thompson Technology Studies, 1995

4. Explanatory PLS: test causal hypotheses
Path coefficients
loadingsweights
Initialize: = avg(y1,…,yq)
= f(x1,…xp)
= avg( 1x1,…, pxp)
yi = f( )
Last: = f( )
iterate

5. Biased coefficients
(Dijkstra , J Econ 1983; Chin et al., ISR 2003)
Under-estimated
path coefficients
Over-estimated
loadings
weights

6. PLS path modeling is “causal-predictive”
Jöreskog & Wold, 1982
Fornell & Bookstein, 1982
Barclay, Higgins & Thompson, 1995

7. PLS RegressionPLS Path Models

8. Explanatory
statistical model:
Theory-based,
empirical testing of
causal hypotheses
Explanatory power:
Strength of
relationship in
statistical model
Predictive model:
Empirical method
for predicting new
observations
Predictive power:
Ability to
accurately predict
new observations

9. Measuring Predictive Power
Holdout data set
Prediction errors
n
i
ii yy
n
RMSE
1
2
ˆ
1 n
i i
ii
y
yy
n
MAPE
1
%100
ˆ1

10. Predictive Aspects of PLS?
Not in PLS software
Not in published research

11. Note: Cross-validation
(blindfolding)
Explanatory context
• Validate measurement
model, structural model
• Estimate communality,
redundancy

12. How to predict holdout data
with PLS?

13. Example: TAM
Gefen & Straub, CAIS 2005
PU
PEOU
USE

14. TAM: Predictive Power?
Training: n=70 Holdout: n=30
RMSE = 2.1
MAPE = 38%

15. Can we do better with a
Predictive Model?

16. Iterative estimation
Node-level errors
Neural Network
Hackl & Westlund TQM 2000 ; Hsu, Chen & Hsieh TQM 2006

17. PLS Neural Net
Hypothesis testing Purely predictive
Causal theory No causal theory
Latent nodes important
Path coefficients matter
Hidden layers – utility
Weights – for prediction
Linear relationships Nonlinear relationships
Restricted paths All possible paths
between layers

18. Predictive Performance
(on holdout)
Method RMSE
PLS (reflective) 2.10
Neural Net 1.94
Linear Regression 1.84
PLS (formative) 2.18
Predicts “3”
Lots of data?