Galit Shmueli 徐茉莉Tsing Hua Distinguished Professor

AnalyticsHumanityInnovation

E. Sun Bank Academic Award

The End of Competitive Advantage Rita McGrath (Columbia U)

Taipei 15/12/2015

“Banking must re-invent itself” 玉山金控總經理／黃男州

“Statistics must re-invent itself"

Research in Data Analytics‘Entrepreneurial’

statistical & data mining modeling (for today’s problems)

Interdisciplinary modeling

Statistical StrategyTo Explain or To Predict?Information QualityRegression with Big Data

ONE-WAY MIRRORS IN ONLINE DATING A Randomized Field Experiment

Management Science, forthcoming

A TREE-BASED APPROACH FOR ADDRESSING SELF-SELECTION IN IMPACT STUDIES WITH BIG DATA

MIS Quarterly, forthcoming

A Tree-Based Approach for Addressing Self-selection in Impact Studies with Big Data

Inbal Yahav Galit Shmueli Deepa ManiBar Ilan University National Tsing Hua U Indian School of Business Israel Taiwan India

Self Selection: The Challenge• Large impact studies

of an intervention• Individuals/firms

choose which group to join

How to identify and adjust for self-selection?

Current Methods: Challenges with Big Data1. Matching leads to severe data loss

2. Suffer from “data dredging”

3. Do not identify variables that drive the selection

4. Assume constant intervention effect

5. Sequential nature is computationally costly

6. Requires user to specify form of selection model

Our Tree-Based Approach: Use a data mining algorithm in a novel way!

Flexible non-parametric selection model

Automated detection of unbalanced variables

Easy to interpret, transparent, visual

Applicable to binary, polytomous, continuous intervention

Useful in Big Data context

Identify heterogeneous effects

Example: Impact of training on financial gains

Experiment: USA govt program randomly assigned eligible candidates to training program• Goal: increase future earnings• Results (LaLonde, 1986) :

Groups statistically equal in terms of demographic & pre-train earnings

Average Training Effect = $1794 (p<0.004)

Tree reveals… High-School Matters!

LaLonde’s naïve approach (experiment)

Tree approachHS dropout

(n=348)HS degree

(n=97)Not trained (n=260) $4554 $4,495 $4,855Trained (n=185) $6349 $5,649 $8,047

Training effect

$1794(p=0.004)

$1,154(p=0.063)

$3,192(p=0.015)

Overall: $1598 (p=0.017)

no yes

High school degree

ONE WAY MIRRORS IN ONLINE DATINGA Randomized Field Experiment

Ravi Bapna, University of MinnesotaJui Ramaprasad, Mcgill UniversityGalit Shmueli, National Tsing Hua

UniversityAkhmed Umyarov, University of

Minnesota

Online Dating

46

of the single population in the US uses online dating to find a partner (Gelles 2011)

%

14/20Online Dating Website

15/20Non-anonymous Browsing (Default)

Profile Visit

Recent visitor:

16/20Anonymous Browsing

Profile Visit

Recent visitor:

NONE

17/20Research Question (in simple words)

How does anonymous browsing affect user behavior?

… and matching?

Formal Research Question

what is the relative causal effect of social inhibitions on search preferences vs. social inhibitions of contact initiation in dating markets?

given known gender asymmetries, how does this effect differ for men vs. women?

19/20

Randomized Field Experiment on Large Online Dating Website

50,000 users receive gift of anonymous browsing

20/20Results

Users treated with anonymity

become disinhibited view more profiles, view more same-sex and interracial mates

get less matcheslose ability to leave a weak signal - especially harmful for women!

Role of anonymity and importance of WEAK SIGNAL

in online platforms