Causal Inference: Understanding Treatment Effects

About
Privacy
Disclaimer

Graph Chatbot

Related lectures (32)

Page 2 of 4

Statistical Analysis of Networks: Link Prediction and Biclustering

Explores link prediction, logistic regression, causal inference, and biclustering in statistical network analysis.

Observational Studies: Pitfalls and Solutions

Explores the challenges of observational studies, emphasizing the importance of randomization and sensitivity analysis in drawing valid conclusions from 'found data'.

Probabilistic Models for Linear Regression

Covers the probabilistic model for linear regression and its applications in nuclear magnetic resonance and X-ray imaging.

Instrumental Variables: Addressing Measurement Error and Reverse Causality

Explores how instrumental variables correct biases from measurement error and reverse causality in regression models.

Variational Inference and Neural Networks

Covers variational inference and neural networks for classification tasks.

Observational Studies: Pitfalls and Solutions

Covers the pitfalls of observational studies, solutions to avoid biases, and the importance of valid conclusions from 'found data'.

Instrumental Variables: Part 1

Introduces instrumental variables to address endogeneity issues, using examples to illustrate practical applications and testing requirements.

Birth Month Impact on Athlete Success

Investigates how birth month influences athlete success, analyzing Japanese athletes' dataset to explore trends in birthdates and occupations.

Randomized Trials and Simpson's Paradox

Covers randomized clinical trials, confounding variables, ethical challenges, and Simpson's paradox in data analysis.

Assessing Market Regulation

Explores the assessment of market regulation, focusing on high-frequency trading and the impact of regulatory changes on liquidity and market quality.