Episode 47: Leveraging Causal Inference to Drive Business Value in Data Science
Value Driven Data Science: Boost your impact. Earn what you’re worth. Rewrite your career algorithm. · Dr Genevieve Hayes
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Show Notes
For most people, data science is synonymous with machine learning, and many see the role of the data scientist as simply being to build predictive models. Yet, predictive analytics can only get you so far. Predicting what will happen next is great, but what good is knowing the future if you don’t know how to change it?
That’s where causal analytics can help. However, causal inference is rarely taught as part of traditional prediction-centric data science training. Where it is taught, though, is in the social sciences.
In this episode, Joanne Rodrigues joins Dr Genevieve Hayes to discuss how techniques drawn from the social sciences, in particular, causal inference, can be combined with data science techniques to give data scientists the ability to understand and change consumer behaviour at scale.
Guest Bio
Joanne Rodrigues is an experienced data scientist with master’s degrees in mathematics, political science and demography. She is the author of Product Analytics: Applied Data Science Techniques for Actionable Consumer Insights and the founder of health technology company ClinicPriceCheck.com.
Highlights
- (00:49) Combining social sciences with data science
- (02:01) Joanne’s journey from social sciences to data science
- (04:15) Understanding causal inference
- (07:40) Real-world applications of causal inference
- (12:22) Challenges in causal inference
- (19:41) Correlation vs. causation in data science
- (26:12) Operationalising randomness in experiments
- (27:16) Observational experiments vs. medical trials
- (27:47) Designing experiments with existing data
- (28:50) Challenges in natural experiments
- (29:55) Ethical considerations in experimentation
- (31:50) Qualitative frameworks in causal inference
- (35:58) Integrating causal inference with machine learning
- (38:59) Common techniques in causal inference
- (41:02) Marketing causal inference to management
- (43:48) Ethical implications of predictive modelling
- (48:08) Final advice for data scientists
Links
- Connect with Joanne on LinkedIn
- Joanne’s website
- Connect with Genevieve on LinkedIn
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