Jakob Zeitler is a Pioneer Fellow at SMARTbiomed and a PhD graduate from the UCL Centre for Artificial Intelligence. In this conversation, we explore the intersection of causal inference and computer science, focusing on its theoretical foundations and practical applications. We discuss how frameworks like Directed Acyclic Graphs (DAGs) and potential outcomes are used to model causality, the differences between observational and experimental studies, and the growing role of causal inference in AI and machine learning. The conversation includes real-world examples, industry use cases, and guidance for applying causal methods in both academic and business settings.

Expect to learn about

- The fundamentals of causal inference, including DAGs and potential outcomes.
- The practical differences between observational and experimental studies.
- How causal reasoning is applied in fields like public health, forecasting, and logistics.
- The challenges and opportunities of integrating causal inference with machine learning.
- Methods such as proximal learning and synthetic control.
- The limitations of causal discovery tools in business contexts.
- How industries are leveraging experimentation and causal inference for decision-making.

Where to find Jakob

Website: https://jakobzeitler.github.io/
LinkedIn: https://www.linkedin.com/in/jakobzeitler/


Where to find Ida

Website: https://idajohnsson.com/
LinkedIn: https://www.linkedin.com/in/ida-johnsson/
X: https://twitter.com/IdaBJohnsson

Timestamps

00:00 Preview
00:00 Intro
00:01 Causal Inference in Computer Science
00:04 DAGs vs. potential outcomes
00:07 Proximal learning
00:16 Theory vs. practice
00:20 Making assumptions & observational data
00:26 The cost of assumptions
00:33 Including disclaimers
00:36 Causality in forecasting
00:41 What applied problems do Computer Scientists work on?
00:43 Causal discovery
00:51 CS vs. economist training
00:59 Causal inference in everyday life

I chat with Colin Gray, Senior Data Scientist at Netflix, as he shares his journey from economics to data science in tech. Discover how asking the right questions shapes decisions, the challenges of modeling human behavior, and how economics provides a lens for tackling complex problems.

Expect to Learn About

- The importance of asking the right questions in data science.
- How economics principles apply to tech and business problems.
- The challenges of identifying causality in noisy data.
- Key skills for thriving in data science and analytics roles.
- The unique role of economists in tech organizations.

Where to find Colin

LinkedIn: https://www.linkedin.com/in/colintgray/


Where to find Ida

Website: https://idajohnsson.com/
LinkedIn: https://www.linkedin.com/in/ida-johnsson/
X: https://twitter.com/IdaBJohnsson

Timestamps

00:00 Preview
00:37 How Colin got interested in Econ
05:18 PhD training vs. selection bias
06:09 What distinguishes economists from other scientists
09:04 Questioning mindset
12:13 Heterogeneity
15:30 Builders vs. thinkers
18:44 Teaching and mentoring
21:54 What Colin looks for when interviewing
25:52 Solving problems as a senior scientist
29:34 Measuring impact
33:59 Should you get a PhD
38:27 Networking
41:08 Causal thinking in everyday life
46:09 Openness to changing your mind

Dimitriy V. Masterov, a fractional tech economist with over 20 years of experience, joins me to discuss how causal inference and economic strategies can revolutionize decision-making in startups and beyond. Dimitriy shares his journey from working with major companies like Domino’s, eBay, and Thumbtack to pioneering innovative solutions in early-stage marketplaces as a fractional chief economist.

We delve into the practical applications of economics, from monetization strategies and pricing models to understanding the nuanced role of causal inference in solving business problems.

Where to find Dimitriy

LinkedIn: https://www.linkedin.com/in/dimitriy-masterov
Website: https://www.mylance.co/frace


Where to find Ida

Website: https://idajohnsson.com/
LinkedIn: https://www.linkedin.com/in/ida-johnsson/
X: https://twitter.com/IdaBJohnsson

Expect to learn about:

- The difference between causal inference and machine learning in solving business problems
- How early-stage startups can leverage economists to refine monetization and growth strategies
- Real-world examples of marketplace experimentation and marketing measurement
- Why startups often overlook the power of causal thinking—and how to change this

Timestamps

00:00 Preview
01:14 Intro
06:16 Causal inference vs. ML
09:29 Working as a Fractional Economist
11:27 Can you trust causal estimates?
14:40 Measuring marketing effectiveness
18:27 Do startups need economists?
24:18 Do people know enough about causal inference?
25:19 Working as an entrepreneur
30:50 Job search tips
35:14 Impact of AI
37:51 Manager vs. IC track
40:47 Economists' business intuition