Professor Jennifer Hill from NYU discusses causality, correlation vs. causation, counterfactuals, Bayesian and ML tools for causal inferences, and a new GUI for causal inferences. Tips on learning more about causal inference, the usefulness of multilevel models, and clarifying assumptions when inferring causality from data are also covered.
Understanding causality is pivotal in data science decision-making.
Differentiating correlation from causation requires randomization for confident causal inferences.
Bayesian Additive Regression Trees enhance causal inference with flexibility and coherent uncertainty estimates.
Deep dives
Importance of Causality in Data Science
Understanding causality is crucial in data science applications. Professor Jennifer Hill discusses how causal questions drive decision-making, emphasizing that many decisions are based on implicit causal reasoning. From designing research to implementing causal models, causality plays a central role in all data science applications.
Challenges in Inferring Causality
Distinguishing correlation from causation poses a challenge, with examples like personal experiences influencing mistaken causal conclusions. The discussion highlights the need for randomization in studies to confidently infer causality, especially in instances like vaccination efficacy where individual experiences may not reflect broader causal relationships.
Bayesian Methods for Causal Inference
Bayesian Additive Regression Trees (BART) emerge as a valuable tool for causal inference, offering flexibility, coherent uncertainty estimates, and efficient overfitting avoidance. Professor Hill's integration of BART within a Bayesian framework enhances its suitability for addressing causal questions, showing promise through empirical performance and adaptability in data analysis challenges.
Learning Causal Inference Through Think Causal Tool
You can explore causal inference and run causal models using the Think Causal tool without needing to code in Python or R. By understanding the limits of data measurements and the importance of humility and transparency, you can gain valuable insights into causality. Taking courses in research methods, measurement, and qualitative research can greatly enhance your understanding of causal inference.
Utilizing Multilevel Models for Causal Inference
Multilevel models are essential for addressing grouped data where observations are not identically and independently distributed. These models allow for a more accurate estimation of uncertainty by considering the correlation between clustered data points. They help in discerning group-level effects and individual-level effects, making it easier to make inferences at different levels of aggregation. By incorporating hierarchical structures, researchers can analyze complex phenomena and improve the accuracy of their causal inferences.
We welcome Dr. Jennifer Hill, Professor of Applied Statistics at New York University, to the podcast this week for a discussion that covers causality, correlation, and inference in data science.
This episode is brought to you by Pachyderm, the leader in data versioning and MLOps pipelines and by Zencastr (zen.ai/sds), the easiest way to make high-quality podcasts.
In this episode you will learn:
• How causality is central to all applications of data science [4:32]
• How correlation does not imply causation [11:12]
• What is counterfactual and how to design research to infer causality from the results confidently [21:18]
• Jennifer’s favorite Bayesian and ML tools for making causal inferences within code [29:14]
• Jennifer’s new graphical user interface for making causal inferences without the need to write code [38:41]
• Tips on learning more about causal inference [43:27]
