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COMM1190 Data, Insights and Decisions

创建日期：2024-05-27 16:00:46

所属分类：计算机computer science

标签： COMM1190

Data, Insights and Decisions

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COMM1190 Data, Insights and Decisions

Week 8: Research Design &
Experimentation I
General housekeeping for online lecture:
• Please turn on your camera for better engagement
• Please switch your microphone to mute to avoid disruption to the class
• Use your microphone or the chat channel to ask questions or make a comment
• Wait for your lecturer to start
610
Flexibility Week
COMM1190 Data, Insights & Decisions
32 4 5 8 97 101
Intro to
Analytics
Data Exploration
and Visualisation
I
Data Exploration
and Visualisation
II
Predictive
Analytics I
Predictive
Analytics II
Research Design
&
Experimentation
I
Research Design
&
Experimentation
II
Data Ethics Data Communication
x
This week (& next)
 Research design & its importance for prescriptive analytics
 Organisations need to answer what-if & evaluation type questions which involve casual questions
 Experiments using randomised control trials (RCTs) are one way to obtain causal effects
 Big data world associated with many opportunities to run experiments (A/B online experiments)
 Role for non-experimental or observational data
 Data availability provides opportunities to exploit natural experiments (quasi experiments)
Week 8 references
 Accessible if interested
 Fiebig, D.G. (2017), “Big data: Will it improve Patient-Centered Care?”,
The Patient: Patient–Centered Outcomes Research, 10, 133-139.
 Haynes, L., Service, O., Goldacre, B. and Torgenson, B. (2012), “Test, Learn, Adapt: Developing public policy with Randomised Controlled Trials”, Cabinet Office, London.
 Varian, H.R. (2016), “Causal inference in economics and marketing”,
PNAS, 113 (27) 7310-7315.
 Referenced for completeness
 Chattopadhyay, R. and Duflo, E. (2004), “Women as policy makers: Evidence from a randomized policy experiment in India”, Econometrica72, 1409-1443.
Research Design &
Experimentation: Introduction
Motivation
Correlation does not imply causation
 Correlation coefficients measure the strength of an association between two variables
 Recall spurious correlation examples in week 4 – obvious that these do not imply cause & effect
 Other examples may be less obvious to dismiss – there is a correlation between advertising & sales but is it causal?
 How do analysts generate evidence that one action will lead to a particular effect on some outcome of interest?
 How are causal effects reliably estimated?
Introduction
 Organisations require answers to questions & input into decision-making
 Research designStrategy of how one addresses these questions by integrating all parts of the analysis to provide the opportunity to deliver answers
 Constituent parts of data analysis
 Subject matter theory to provide context
 Appropriate data
 Modelling approach that is appropriate for the data & has the
potential to deliver answers – this design question our focus
Introduction…
 What-if & evaluation type questions are crucial for prescriptive analysis
 Involves causal questions requiring estimates of causal effects
 What if a change is made how will that effect future outcomes?
 What impact did an intervention have? Was a policy change that was implemented effective?
 Design particularly important for prescriptive analysis
 Research design: Can causal question being asked be answered by available data & planned modelling approach?
Introduction…
 Case study: Customer churning/retention problem
 Descriptive: Is there a problem with customer churn?
 Predictive: Which customers are at most risk of churning?
 Prescriptive: Which customers are most likely to be
retained if offered incentives to stay? Once implemented
was the incentive cost effective?
 Other examples of such questions
 Q(a) Will it be profitable if on-line advertising is increased?
 Q(b) Will a back-to-work intervention help people get a job?
 Q(c) How much should homeowners living near to a chemical plant be compensated for a chemical spill?
Introduction…
 Experiments are one way to obtain causal effects
 Data deluge in part due to greater opportunities for organizations to collect data & run experiments
 An online A/B experiment could address Q(a)
 Q(b) would require a field experiment
 Some causal questions not amenable to an experiment
 Q(c) is such an example but may be able to get reliable answers from available observational data
 Important design issues related to natural experiments
Introduction…
 Experimental mindset
 Partial equilibrium approach - complex questions broken up into tractable components
 Power of randomisation to control for confounders
 Test, learn, adapt cycle (evidence-based decision-making –as discussed in COMM1110)
Regression: Recall week 4
Regression as an analytical tool
 Linear regression model
= 0 + 1 +
 Useful descriptive device to capture bivariate relationships
 Consider sales () & TV advertising ()(advertising.xlsx)
 Q8.1 What key features of
the data are revealed by
the scatter plot?
0
10
20
30
Sa
le
s
in
th
ou
sa
nd
s
0 100 200 300
Advertising budget in thousands of dollars
sales Fitted values
Sales versus TV advertising
Regression as an analytical tool...
 Have specified a model
 Sales in a market is a linear function of TV advertising
 OLS provides best fit conditional on this model
 Fine as a descriptive device providing stylized facts
 Provides evidence of positive correlation (linear association) between sales & advertising (̂1 = 0.048)
 Prediction of sales for out-of-sample market?
� = 7.033 + 0.048
 A market where = 100 → � = 11.833
Regression as an analytical tool...
 But may want models to do even more – causality & “what-if” counterfactuals
 What happens to sales in a particular market if TV advertising were increased?
 Doesn’t our regression model reliably answer this question?
 At least 2 threats to interpreting ̂1 as causal
 Confounding variables leading to omitted variable bias
 Is estimated advertising effect biased?
 Maybe prices are varying across markets & these are correlated with advertising & hence effects of prices & advertising are confounded
 Reverse causality
 What if markets with low sales increase advertising?
Regression as an analytical tool...
 Prediction models aim to minimize prediction inaccuracy
 Interest is focussed on � not ̂1
 A regression tree could be used provide accurate predictions, but not to address questions of causality
 Questions about causality tend to be more difficult to answer than prediction/forecasting problems
 Need a conceptual basis to guide our approach
 Design issues become very important
Causality & Experimentation
Causality & notion of ceteris paribus
 Causality as defined by philosopher David Lewis:
 “Causation is something that makes a difference, and the
difference it makes must be a difference from what would
have happened without it“
 In evaluating an intervention (or policy change) think of
counterfactual outcomes & what-if questions
 Sales with & without the increase in advertising
 In regression context want to define causal effect of on
 How does variable change if is changed but all other
relevant factors are held constant
 Requires (at a minimum) & to be unrelated
Causality & notion of ceteris paribus...
 Multiple regression provides one approach to better estimate the causal effect of interest (say 1)
= 0 + 11 + 22 + ⋯+ +
 Now more likely 1 & are uncorrelated
 Estimate of 1 controls for other variables & better represents pure impact of 1 (have purged any indirect effect because of correlation with other variables)
 An even better approach is to conduct an experiment
 Randomised control trials (RCTs) suggested as gold standard
 Important to define causal effect of interest & describe how an experiment would be designed to infer causal effect in question
Experiment 1
 Impact of back-to-work program on employment
 “If a person chosen from population of those looking for work is given access to a back-to-work program, will that increase their chance of employment?”
 Implicit assumption: all other factors influencing employment (experience, ability, local employment prospects,...) are held fixed
 Experiment:
 Choose a group of workers looking for work
 Randomly assign them to access the program or not
 Compare employment outcomes in next period
 Experiment works because characteristics of people are unrelated to whether they receive program or not
RCT evaluating back-to-work program
From Haynes et al. (2012)
Experiment 2
 A/B testing of a website landing page
 “If a business rearranges its current website, by how much will this change the conversion rate (new customers)?”
 Implicit assumption: all other factors that influence who visits the website are held fixed
Experiment 2….
 Experiment:
 Design the new webpage
 Randomly assign different users to old (A) & new (B) website
 Compare conversion rates i.e. new customers
 Experiment works because characteristics of users are unrelated to which website is seen
 In online environments relatively easy to conduct
Experiment 3
 Measuring returns to education
 “If a person chosen from the population is given another year of education, by how much will his or her wage increase?”
 Implicit assumption: all other factors that influence wages such as experience, family background, intelligence etc. are held fixed
 Experiment:
 Choose a group of people
 Randomly assign different amounts of education to them!!!
 Compare wage outcomes
 Random assignment is infeasible in this case
 Experiments are not always possible or ethical
Conducting RCTs
 Decide on form of intervention (new program/new website versus status quo)
 Flexibility in designing intervention as new program/website may involve several new features (week 8 workshop, Q1)
 Determine outcome of interest (employment/conversion rates)
 Decide on randomisation unit (workers/customers)
 Determine sample size & randomly assign units to
treatment (new program/new website) & control (no program/old website) groups
 Care required in this step (week 8 workshop, Q2)
Conducting RCTs…
 Compare outcomes to determine treatment effect
 Differences in outcomes can reasonably be attributable to treatment as other aspects of data controlled by researcher
 Decide on whether to adapt (implement program/use new website) or not on basis of findings
 See asynchronous lecture (Experiments in a Big Data World) for Case Study 2 & week 8 workshop, Q3
Experimental evidence

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