The hottest Causal Inference Substack posts right now

And their main takeaways
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Top Science Topics

My Class About Causal Inference

By Reason Alone • 42 implied HN points • 13 Feb 25
🚌 Education Teaching Learning Causal Inference Social Sciences Mathematics
  1. Teaching causal inference helps students understand the relationship between cause and effect in social sciences. It's important to make complex ideas relatable to engage younger audiences.
  2. Using visual aids, like graphs, can enhance understanding of complicated topics, especially in a classroom setting. Students can connect better with the material when it’s presented visually.
  3. Recommended readings and real-world examples, like the draft lottery, can spark curiosity in students. Sharing interesting studies can help them see the relevance of these concepts in everyday life.
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Difference-in-Differences: No Anticipation with Parallel Trends (with a simulation and equations)

Scott's Substack • 117 implied HN points • 31 Jan 24
🔬 Science Causal Inference Statistics Simulation Equations Data Analysis
  1. No anticipation means the baseline period is equal to Y(0) not Y(1)
  2. Difference-in-differences coefficient equals ATT in the post period for the treatment group plus parallel trends bias minus ATT in the incorrectly specified baseline period
  3. Difference-in-differences always requires three assumptions to point identify the ATT: SUTVA, Parallel trends, and No Anticipation

Workshop announcement: $95 price for non-tenure track or professors with higher teaching loads

Scott's Substack • 117 implied HN points • 24 Jan 24
🚌 Education Workshop Teaching Causal Inference Pricing Learning
  1. Workshop offering discounted price of $95 for non-tenure track professors or those with high teaching loads
  2. Workshop covers topics like potential outcomes model, unconfoundedness, and instrumental variables
  3. Teaching style focuses on comprehension, confidence, and competency in applying causal inference methods

Mixtape Mailbag #8: Continuous Triple Differences

Scott's Substack • 39 implied HN points • 05 Feb 24
🔬 Science Causal Inference Data Analysis Research Methods Econometrics
  1. Triple difference design can be used with continuous treatment by defining the parameters based on dosage levels.
  2. When treatment is continuous, the target parameter shifts from average treatment effect to average causal response function.
  3. Continuous treatments require careful definition of parameters to compare different dosages along a treatment curve.

Unlock Causal Inference: 3 Obstacles, 8 Insights & 1 Resource

Mindful Modeler • 159 implied HN points • 29 Nov 22
🔬 Science Causal Inference Statistics Models Education Resources
  1. Causal inference can be challenging to start due to various obstacles like diverse approaches and neglected education on the topic.
  2. Understanding causal inference involves adjusting your modeling mindset to view it as a unique approach rather than just adding a new model.
  3. Key insights for causal inference include the importance of directed acyclic graphs, starting from a causal model, and the challenges of estimating causal effects from observational data.

Approaches to uncertainty, causality and interpretability with supervised learning

Mindful Modeler • 159 implied HN points • 04 Oct 22
🕹 Technology Causal Inference Interpretability
  1. Supervised learning can go beyond prediction to offer uncertainty quantification, causal effect estimation, and interpretability using model-agnostic tools.
  2. Uncertainty quantification with conformal prediction can turn 'weak' uncertainty scores into rigorous prediction intervals for machine learning models.
  3. Causal effect estimation with double machine learning allows for correction of biases in causal effect estimation through supervised machine learning.

Data For AI

Gradient Flow • 59 implied HN points • 31 Mar 22
🕹 Technology Data Engineering AI Machine Learning Causal Inference
  1. Data engineering and data infrastructure are foundational for AI and machine learning success. Businesses need to focus on data integration to scale their use of AI and machine learning.
  2. New tools and frameworks like DoWhy for causal inference and the AI Risk Management Framework from NIST are shaping how we manage AI risks and explore causal learning.
  3. State-of-the-art AI systems require additional training data to achieve top-notch results across various benchmarks. Additional data is crucial for enhancing AI performance.

[🔥Hot Takes] Deep learning outperforms linear regression for causal inference and tabular data??

Data Science Daily • 0 implied HN points • 02 Mar 23
🕹 Technology Data science Deep Learning Causal Inference Machine Learning Research
  1. Deep learning can outperform linear regression for causal inference in tabular data.
  2. Different perspectives exist in the debate between deep learning and traditional models like XGBoost.
  3. The study suggests that deep learning models like CNN, DNN, and CNN-LSTM may offer better performance in certain scenarios.