Published Nov 24, 2020

A casual conversation concerning causal inference

Join Lucy D’Agostino McGowan as she delves into the world of causal inference, unraveling the complexities of unmeasured confounding in observational studies, addressing the vital role of sensitivity analyses, and highlighting ethical challenges and communication strategies in data reporting.

Episode Highlights

Topics covered

Episode Highlights

Related Episodes

Causal inference
Answers 383 questions
Data science for intuitive user experiences
Answers 383 questions
Real-time conversational insights from phone call data
Answers 383 questions
Explainable AI that is accessible for all humans
Answers 383 questions
R, Data Science, & Computational Biology
Answers 383 questions
What exactly is "data science" these days?
Answers 383 questions
Visualizing and understanding RNNs
Answers 383 questions
It's time to talk time series
Answers 383 questions
The mathematics of machine learning
Answers 383 questions
The perplexities of information retrieval
Answers 383 questions
AI trailblazers putting people first
Answers 383 questions
Fighting bias in AI (and in hiring)
Answers 383 questions
IBM's AI for detecting neurological state
Answers 383 questions
Learning the language of life
Answers 383 questions
Achieving provably beneficial, human-compatible AI
Answers 383 questions

Dexa/Practical AI

A casual conversation concerning causal inference

Topics covered

Popular Clips

Communicating Data Effectively

Causal Inference Workshop

Weekend Highlights

Causal Inference Challenges

Trust in Uncertainty

Map Misinterpretation Issues

Causal Thinking Insights

Unmeasured Confounding

Effective Communication Strategies

Trust in Data

Data Interpretation Ethics

Causal Inference Trends

Human Data Interaction

Causal Inference Tools

Causal Inference Explained

Episode Highlights

Unmeasured Confounding

Data Communication Challenges

Causal Inference Basics

Related Episodes

Causal inference

Data science for intuitive user experiences

Real-time conversational insights from phone call data

Explainable AI that is accessible for all humans

R, Data Science, & Computational Biology

What exactly is "data science" these days?

Visualizing and understanding RNNs

It's time to talk time series

The mathematics of machine learning

The perplexities of information retrieval

AI trailblazers putting people first

Fighting bias in AI (and in hiring)

IBM's AI for detecting neurological state

Learning the language of life

Achieving provably beneficial, human-compatible AI

A casual conversation concerning causal inference

Topics covered

Popular Clips

Episode Highlights

Unmeasured ConfoundingLucy D’Agostino McGowan discusses the complexities of unmeasured confounders in observational studies and how they can skew results. She shares strategies to address these confounders, emphasizing the importance of sensitivity analyses and propensity scores.

Unmeasured Confounding

Data Communication ChallengesLucy D’Agostino McGowan discusses the challenges of handling misinformation and the ethical considerations in statistical reporting. She emphasizes the importance of effective communication strategies to ensure accurate public understanding of data.

Data Communication Challenges

Causal Inference BasicsLucy D’Agostino McGowan, a professor at Wake Forest University, joins the podcast to explore the intricacies of causal inference. She discusses how this concept differs from associations and predictions, using examples like vaccine studies to illustrate its importance.

Causal Inference Basics

Related Episodes