2022 Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction
1. Causality: The Basic Framework
– 발표자: 최성식(발표자료)
2. A Brief History of the Potential Outcomes Approach to Causal inference
– 발표자: 이경선(발표자료)
3. A Classification of Assignment Mechanisms
– 발표자: 박진원(발표자료)
4. A Taxonomy of Classical Randomized Experiments
– 발표자: 공인성(발표자료)
5. Fisher’s Exact P-Values for Completely Randomized Experiments
– 발표자: 정연호(발표자료)
6. Neyman’s Repeated Sampling Approach to Completely Randomized Experiments
– 발표자: 박찬무(발표자료)
7. Regression methods for completely randomized experiments
– 발표자: 김건웅(발표자료)
8. Model-Based Inference for Completely Randomized Experiments
– 발표자: 양동윤(발표자료)
9. Stratified Randomized Experiments
– 발표자: 박석훈(발표자료)
10. Pairwise Randomized Experiments
– 발표자: 이종진(발표자료)
11. Case Study: An Experimental Evaluation of a Labor Market Program
– 발표자: 이지후(발표자료)
12. Unconfounded Treatment Assignment
-발표자: 정휘창(발표자료)
13. Estimating the Propensity Score
-발표자: 박유하(발표자료)
14. Assessing Overlap in Covariate Distributions
-발표자: 이경선(발표자료)
15. Matching to Improve Balance in Covariate Distributions
-발표자: 양동윤(발표자료)
16. Trimming to Improve Balance in Covariate Distributions
-발표자: 박유하(발표자료)
18. Matching Estimators
-발표자: 박찬무(발표자료)
19. A General Method for Estimating Sampling Variances for Standard Estimators for Average Causal Effects
-발표자: 이지후(발표자료)
20. Inference for General Causal Estimands
-발표자: 공인성(발표자료)
21. Assessing Unconfoundedness
-발표자: 정휘창(발표자료)
22. Sensitivity Analysis and Bounds
-발표자: 최성식(발표자료)
23. Instrumental Variables Analysis of Randomized Experiments with One-Sided Noncompliance
-발표자: 박진원(발표자료)
24. Instrumental Variables Analysis of Randomized Experiments with Two-Sided Noncompliance
-발표자: 이종진(발표자료)
25. Model-based analysis in instrumental variable settings: randomized experiments with two-sided noncompliance
-발표자: 김건웅(발표자료)
2021 Causal Inference What if
1. A definition of causal effect, 2. Randomized experiments, 3. Observational studies
-발표자: 김건웅(발표자료)
4. Effect modification
-발표자: 최성식(발표자료)
5. Interaction
-발표자: 양동윤(발표자료)
6. Graphical representation of causal effects
-발표자: 공인성(발표자료)
7. Confounding
-발표자: 박진원(발표자료)
8. Selection bias
-발표자: 박유하(발표자료)
9. Measurement bias
-발표자: 이지후(발표자료)
10. Random variability
-발표자: 정휘창(발표자료)
11. Why model?
-발표자: 이종진(발표자료)
12. IP weighting and marginal structures models
-발표자: 박석훈(발표자료)
13. Standarization and the parametric g-formula
-발표자: 양동윤(발표자료)
14. G-estimation of structural nested models
-발표자: 정휘창(발표자료)
15. Outcome regression and propensity scores
-발표자: 이종진(발표자료)
16. Instrumental variable estimation
-발표자: 박석훈(발표자료)
17. Causal Survival Analysis
-발표자: 박유하(발표자료)
18. Variable selection for causal inference
-발표자: 이지후(발표자료)
19. Time-varying treatments
-발표자: 최성식(발표자료)
2020 Bayesian Data Analysis
2. Single-parameter models
-발표자: 이경민, 박지원(발표자료)
3. Multiparameter models
-발표자: 양동윤(발표자료)
4. Asymptotics and connections to non-Bayesian approaches
-발표자: 김건웅(발표자료)
5. Hierarchical models
-발표자: 공인성(발표자료)
6. Model checking
-발표자: 박석훈(발표자료)
10. Introduction to Bayesian computation
-발표자: 이지후(발표자료)
11. Basic of Markov chain simulation
-발표자: 이종진(발표자료)
12. Computationally efficient Markov chain simulation
-발표자: 최성식(발표자료)
13. Modal and distributional approximations
-발표자: 정휘창(발표자료)
2018 Mathematical Foundations of Infinite-Dimensional Statistical Models
1. Nonparametric Statistical Models
-발표자: 김성현(발표자료)
2. Gaussian Processes
2.1 Definitions, Separability, 0-1 Law, Concentration
-발표자: 이상엽(발표자료)
2.2 Isoperimetric Inequality with Applications to Concentration
-발표자: 이종진(발표자료)
2.3 The Metric Entropy Bound for Suprema of Sub-Gaussian Processes
-발표자: 백규승(발표자료)
2.4 Anderson’s Lemma, Comparison and Sudakov’s Lower Bound
-발표자: 김보영(발표자료)
2.5 The Log-Sobolev Inequality and Further Concentration
2.7 Asymptotics for Extremes of Stationary Gaussian Processes
-발표자: 김사라(발표자료)
2017 Neural Network Learning: Theoretical Foundations
2. The Pattern Classification Problem
3. The Growth Function and VC-Dimension
4. General Upper Bounds on Sample Complexity
5. General Lower Bounds on Sample Complexity
6. The VC-Dimension of Linear Threshold Networks
7. Bounding the VC-Dimension using Geometric Techniques
8. Vapnik-Chervonenkis Dimension Bounds for Neural Networks
9. Classification with Real-Valued Functions
10. Covering Numbers and Uniform Convergence
11. The Pseudo-Dimension and Fat-Shattering Dimension
12. Bounding Covering Numbers with Dimensions
13. The Sample Complexity of Classication Learning
-발표자: 온일상(발표자료)
15. Model Selection
-발표자: 김지수(발표자료)
16. Learning Classes of Real Functions
17. Uniform Convergence Results for Real Function Classes
-발표자: 정구환(발표자료)
18. Bounding Covering Numbers
20. Convex Classes
21. Other Learning Problems
-발표자: 김동하(발표자료)
22. Efficient Learning
23. Learning as Optimization
-발표자: 김영근(발표자료)
24. The Boolean Perceptron
25. Hardness Results for Feed-Forward Networks
26. Constructive Learning Algorithms for Two-Layer Networks
-발표자: 권용찬(발표자료)