Causal Inference

POL SCI 200C. Causal Inference for Political Science (4)

Very often the goal of social science research is to establish or measure the causal effect of one variable on another. In 200C you will learn when you can and cannot say that an estimate represents a “causal effect”. You will learn the key assumptions required for making causal claims, and a variety of methods for estimating causal quantities. In particular we focus on cases where one is unfortunately not necessarily able to run a randomized experiment

POL SCI 200E. Experimental Design for Political Science (4)

We return to an emphasis on causal inference with a focus on the theory and practice of running your own experiments (200E). Here you will learn to design, analyze, and implement experiments of all types, including lab experiments, survey experiments, and field experiments.

STATS M243. Logic, Causation, and Probability (4)

(Same as Epidemiology M204.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: Epidemiology 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.

EPIDEM 203. Topics in Theoretical Epidemiology (2)
Lecture, two hours. Selected topics from current research areas in epidemiologic theory and quantitative methods. Topics selected from biologic models, epidemiologic models, problems in inference, model specification problems, design issues, analysis issues, and confounding. May be repeated for credit with consent of instructor. S/U grading.

EPIDEM M204. Logic, Causation, and Probability (4)
(Same as Statistics M243.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: course 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.

SOCIOL 212B. Quantitative Data Analysis (4)

Lecture, three hours; discussion, one hour. Enforced requisite: course 212A. Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of American Sociological Review or similar journal article. Topics include missing data; binomial, multinomial, and ordinal logistic regression; factor analysis and scale construction; methods for causal inference, including fixed effects and propensity score matching; and primer on advanced topics, including structural equations and multilevel models. S/U or letter grading.

ECON 203C. Introduction to Econometrics: Systems Models (4)

Lecture, three hours. Multivariate regression, simultaneous equation estimation, identification, and latent variables. S/U or letter grading.

Data Science

STATS 201B. Statistical Modeling and Learning (4)

Lecture, three hours; discussion, one hour. Requisites: courses 200A, 201A. Methods of model fitting and parameter estimation, with emphasis on regression and classification techniques, including those from machine learning. Interest in either obtaining suitable conditional expectation function or estimating meaningful parameters of underlying probabilistic model to make inferences or predictions from data. Focus on what is to be done when linear models are not appropriate and may produce misleading estimates. Coverage of classical must know model fitting and parameter estimation techniques such as maximum likelihood fitting of generalized linear models. Exploration of broader regression/classification techniques that have been ubiquitous in machine learning literature, with special attention to regularization and kernelized methods. S/U or letter grading.

STATS 202A. Statistics Programming (4)

Lecture, three hours. Topics include programming environments/languages such as UNIX, UNIX shell, Python, R, and Processing and data technologies/formats such as relational databases/SQL and XML, with emphasis on complex data types, including large collections of textual data, GPS traces, network logs, and various online sources. S/U or letter grading.

STATS 218. Statistical Analysis of Networks (4)

Lecture, three hours. Limited to graduate students. Introduction to analysis of social structure, conceived in terms of social relationships. Major concepts of social network theory and mathematical representation of social concepts such as role and position. Use of graphical representations of network information. S/U or letter grading.

SOCIOL 208A. Social Network Methods (4)
Lecture, three hours; laboratory, one hour. Requisites: courses 210A, 210B. Techniques for measuring characteristics of networks and positions in networks. Centrality of positions, centralization and density of networks, structural equivalence, cliques. Readings of exemplars of network research. Computer programs. S/U or letter grading.

SOCIOL 208B. Social Network Methods (4)
Lecture, three hours; laboratory, one hour. Requisites: courses 210A, 210B. Techniques for measuring characteristics of networks and positions in networks. Centrality of positions, centralization and density of networks, structural equivalence, cliques. Readings of exemplars of network research. Computer programs. S/U or letter grading.

COMM 155. Artificial Intelligence and New Media (4)
(Formerly numbered Communication Studies 155.) Lecture, three hours. Review of origin and modern development of artificial intelligence (AI) and its recent breakthroughs, with special emphasis on its usages of media industry (personalization, recommendation, and target advertising). Study includes technical merits and controversies such as ethical and moral issues of AI, privacy concerns in data collection, and fair use of AI in general. P/NP or letter grading.

Demographic Methods

STATS 206. Modern Survey Methods (4)

Lecture, three hours. Requisites: courses 201A, 201B. Advancements in modern survey methodology. Examination of traditional approaches and consideration of cutting-edge solutions in fields of research in survey methodology. Development of students’ own research. S/U or letter grading.

STATS 201A. Research Design, Sampling, and Analysis (4)

Lecture, three hours. Designed for graduate students. Basic principles, ANOVA block designs, factorial designs, unequal probability sampling, regression estimation, stratified sampling, and cluster sampling. S/U or letter grading.

STATS 203. Large Sample Theory, Including Resampling (4)

(Formerly numbered 200C.) Lecture, three hours. Requisite: course 200B. Asymptotic properties of tests and estimates, consistency and efficiency, likelihood ratio tests, chi-squared tests. S/U or letter grading.

STATS 206. Modern Survey Methods (4)

Lecture, three hours. Requisites: courses 201A, 201B. Advancements in modern survey methodology. Examination of traditional approaches and consideration of cutting-edge solutions in fields of research in survey methodology. Development of students’ own research. S/U or letter grading.

POL SCI 200D. Maximum Likelihood for Social Science (4)
Seminar, three hours; field work, eight hours. Introduction to theory and practice of maximum likelihood analysis in political science, including discrete choice models, event count models, and duration models. Lectures combine traditional formal mathematical derivations of various estimators and their properties with Monte Carlo simulations and discussion of applications and practice. S/U or letter grading.

SOCIOL 212B. Quantitative Data Analysis (4)

Lecture, three hours; discussion, one hour. Enforced requisite: course 212A. Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of American Sociological Review or similar journal article. Topics include missing data; binomial, multinomial, and ordinal logistic regression; factor analysis and scale construction; methods for causal inference, including fixed effects and propensity score matching; and primer on advanced topics, including structural equations and multilevel models. S/U or letter grading.

SOCIOL 213A. Introduction to Demographic Methods (4)

(Formerly numbered 213A.) (Same as Biostatistics M208 and Community Health Sciences M208.) Lecture, four hours. Preparation: one introductory statistics course. Introduction to methods of demographic analysis. Topics include demographic rates, standardization, decomposition of differences, life tables, survival analysis, cohort analysis, birth interval analysis, models of population growth, stable populations, population projection, and demographic data sources. Letter grading.

SOCIOL 213C. Techniques of Demographic and Ecological Analysis (4)

Requisite: course 210A. Procedures and techniques for collection, evaluation, and analysis of demographic and ecological data; models of population and ecological structure and change; applications to study of social structure and social change.

PUB PLC M218. Research Design and Methods for Social Policy (4)

(Same as Urban Planning M204.) Lecture, three hours; outside study, nine hours. Limited to graduate students. How to become more sophisticated consumers and producers of qualitative and quantitative policy research. In first half of course, formal principles of research design; in second half, various data collection methods, including ethnography, interviewing, and survey design. Letter grading.

EPIDEM M211. Statistical Methods for Epidemiology (4)
(Same as Statistics M250.) Lecture, four hours. Preparation: two terms of statistics (such as Biostatistics 100A, 100B). Enforced requisites: courses 200B, 200C. Concepts and methods tailored for analysis of epidemiologic data, with emphasis on tabular and graphical techniques. Expansion of topics introduced in courses 200B and 200C and introduction of new topics, including principles of epidemiologic analysis, trend analysis, smoothing and sensitivity analysis. S/U or letter grading.

Sociology Courses

208A. Social Network Methods (4)
Lecture, three hours; laboratory, one hour. Requisites: courses 210A, 210B. Techniques for measuring characteristics of networks and positions in networks. Centrality of positions, centralization and density of networks, structural equivalence, cliques. Readings of exemplars of network research. Computer programs. S/U or letter grading.

208B. Social Network Methods (4)
Lecture, three hours; laboratory, one hour. Requisites: courses 210A, 210B. Techniques for measuring characteristics of networks and positions in networks. Centrality of positions, centralization and density of networks, structural equivalence, cliques. Readings of exemplars of network research. Computer programs. S/U or letter grading.

210C. Intermediate Statistical Methods III (4)

Lecture, four hours. Requisite: course 210B. Survey of advanced statistical methods used in social research, with focus on problems for which classical linear regression model is inappropriate, including categorical data, structural equations, longitudinal data, incomplete and erroneous data, and complex samples. S/U or letter grading

212A. Quantitative Data Analysis (4)

Lecture, three hours; discussion, one hour. Enforced requisites: courses 210A, 210B. Course 212A is enforced requisite to 212B. Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper proposal in style of “American Sociological Review” or similar journal article. Topics include simple tabular analysis, correlation, log-linear analysis, ordinary least squares regression, regression with interactions, robust regression, diagnostic procedures, and methods for handling complex sample survey designs. In Progress grading (credit to be given only on completion of course 212B).

212B. Quantitative Data Analysis (4)

Lecture, three hours; discussion, one hour. Enforced requisite: course 212A. Analysis and interpretation of primarily nonexperimental quantitative data, with focus on sample survey and census data. Extensive practice at utilizing statistical methods encountered in previous courses, culminating in term paper in style of American Sociological Review or similar journal article. Topics include missing data; binomial, multinomial, and ordinal logistic regression; factor analysis and scale construction; methods for causal inference, including fixed effects and propensity score matching; and primer on advanced topics, including structural equations and multilevel models. S/U or letter grading.

213A. Introduction to Demographic Methods (4)

(Formerly numbered 213A.) (Same as Biostatistics M208 and Community Health Sciences M208.) Lecture, four hours. Preparation: one introductory statistics course. Introduction to methods of demographic analysis. Topics include demographic rates, standardization, decomposition of differences, life tables, survival analysis, cohort analysis, birth interval analysis, models of population growth, stable populations, population projection, and demographic data sources. Letter grading.

213C. Techniques of Demographic and Ecological Analysis (4)

Requisite: course 210A. Procedures and techniques for collection, evaluation, and analysis of demographic and ecological data; models of population and ecological structure and change; applications to study of social structure and social change.

Economics Courses

200. Mathematical Methods in Economics (4)

Lecture, three hours. Should be taken prior to enrollment in course 201A. Examination of mathematical methods used in graduate-level courses in microeconomics, macroeconomics, and quantitative methods. Topics include real analysis, linear algebra and matrices, calculus of many variables, static optimization, convex analysis, and dynamics and dynamic optimization. S/U grading.

200B. Mathematical Methods in Economics II (4)

Lecture, three hours; laboratory, two hours. Should be taken prior to or concurrent with course 201B. Linear algebra and its application to linear difference equations. Basic real analysis, normed vector space/Banach space, Hahn/Banach theorem, Schauder fixed point theorem, and theory of correspondences. S/U grading.

203A. Probability and Statistics for Econometrics (4)

Lecture, three hours. Provides statistical tools necessary to understand econometric techniques. Random variables, distribution and density functions, sampling, estimators, estimation techniques, hypothesis testing, and statistical inference. Use of economic problems and examples. S/U or letter grading.

203B. Introduction to Econometrics: Single Equation Models (4)

Lecture, three hours. Estimation of basic linear regression model, testing hypotheses, generalized least squares, serial correlation, heteroskedasticity, multicollinearity, error-in-variables, distributed lags, qualitative dependent variables, and forecasting. S/U or letter grading.

203C. Introduction to Econometrics: Systems Models (4)

Lecture, three hours. Multivariate regression, simultaneous equation estimation, identification, and latent variables. S/U or letter grading.

Political Science Courses

200C. Causal Inference for Political Science (4)

Very often the goal of social science research is to establish or measure the causal effect of one variable on another. In 200C you will learn when you can and cannot say that an estimate represents a “causal effect”. You will learn the key assumptions required for making causal claims, and a variety of methods for estimating causal quantities. In particular we focus on cases where one is unfortunately not necessarily able to run a randomized experiment

200D. Maximum Likelihood for Social Science (4)
Seminar, three hours; field work, eight hours. Introduction to theory and practice of maximum likelihood analysis in political science, including discrete choice models, event count models, and duration models. Lectures combine traditional formal mathematical derivations of various estimators and their properties with Monte Carlo simulations and discussion of applications and practice. S/U or letter grading.

200E. Experimental Design for Political Science (4)

We return to an emphasis on causal inference with a focus on the theory and practice of running your own experiments (200E). Here you will learn to design, analyze, and implement experiments of all types, including lab experiments, survey experiments, and field experiments.

200F. Advanced Statistical Topics for Social Science (4)
Seminar, three hours; field work, eight hours. Preparation: courses 200A through 200E. Topics vary according to student interest. May be repeated for credit. S/U or letter grading.

Communication

155. Artificial Intelligence and New Media (4)
(Formerly numbered Communication Studies 155.) Lecture, three hours. Review of origin and modern development of artificial intelligence (AI) and its recent breakthroughs, with special emphasis on its usages of media industry (personalization, recommendation, and target advertising). Study includes technical merits and controversies such as ethical and moral issues of AI, privacy concerns in data collection, and fair use of AI in general. P/NP or letter grading.

Recommended Prerequisite Statistics Courses

20. Introduction to Statistical Programming with R (4)

Lecture, three hours; discussion, one hour. Enforced requisite: course 10, 12, or 13. Designed to prepare students for upper-division work in statistics. Introduction to use of R, including data management, simple programming, and statistical graphics in R. P/NP or letter grading.

101A. Introduction to Data Analysis and Regression (Effective Spring 2019) (4)

Lecture, three hours; discussion, one hour. Requisites: course 10 or 12 or 13 or Economics 41 or score of 4 or higher on Advanced Placement Statistics Examination, and course 20. Recommended: course 102A. Applied regression analysis, with emphasis on general linear model (e.g., multiple regression) and generalized linear model (e.g., logistic regression). Special attention to modern extensions of regression, including regression diagnostics, graphical procedures, and bootstrapping for statistical influence. P/NP or letter grading.

Statistics Courses

200A. Applied Probability (4)

Lecture, three hours. Requisite: course 100A or Mathematics 170A. Limited to graduate statistics students. Simulation, renewal theory, martingale, and selected topics from queuing, reliability, speech recognition, computational biology, mathematical finance, epidemiology. S/U or letter grading.

200B. Theoretical Statistics (4)

Lecture, three hours. Sufficiency, exponential families, least squares, maximum likelihood estimation, Bayesian estimation, Fisher information, Cramér/Rao inequality, Stein’s estimate, empirical Bayes, shrinkage and penalty, confidence intervals. Likelihood ratio test, p-value, false discovery, nonparametrics, semi-parametrics, model selection, dimension reduction. S/U or letter grading.

201A. Research Design, Sampling, and Analysis (4)

Lecture, three hours. Designed for graduate students. Basic principles, ANOVA block designs, factorial designs, unequal probability sampling, regression estimation, stratified sampling, and cluster sampling. S/U or letter grading.

201B. Statistical Modeling and Learning (4)

Lecture, three hours; discussion, one hour. Requisites: courses 200A, 201A. Methods of model fitting and parameter estimation, with emphasis on regression and classification techniques, including those from machine learning. Interest in either obtaining suitable conditional expectation function or estimating meaningful parameters of underlying probabilistic model to make inferences or predictions from data. Focus on what is to be done when linear models are not appropriate and may produce misleading estimates. Coverage of classical must know model fitting and parameter estimation techniques such as maximum likelihood fitting of generalized linear models. Exploration of broader regression/classification techniques that have been ubiquitous in machine learning literature, with special attention to regularization and kernelized methods. S/U or letter grading.

202A. Statistics Programming (4)

Lecture, three hours. Topics include programming environments/languages such as UNIX, UNIX shell, Python, R, and Processing and data technologies/formats such as relational databases/SQL and XML, with emphasis on complex data types, including large collections of textual data, GPS traces, network logs, and various online sources. S/U or letter grading.

202B. Matrix Algebra and Optimization (4)

Lecture, three hours. Recommended requisite: course 202A. Survey of computational methods that are especially useful for statistical analysis, with implementations in statistical package R. Topics include matrix analysis, multivariate regression, principal component analysis, multivariate analysis, and deterministic optimization methods. S/U or letter grading.

202C. Monte Carlo Methods for Optimization (4)

Lecture, three hours. Requisite: course 202B. Monte Carlo methods and numerical integration. Importance and rejection sampling. Sequential importance sampling. Markov chain Monte Carlo (MCMC) sampling techniques, with emphasis on Gibbs samplers and Metropolis/Hastings. Simulated annealing. Exact sampling with coupling from past. Permutation testing and bootstrap confidence intervals. S/U or letter grading.

203. Large Sample Theory, Including Resampling (4)

(Formerly numbered 200C.) Lecture, three hours. Requisite: course 200B. Asymptotic properties of tests and estimates, consistency and efficiency, likelihood ratio tests, chi-squared tests. S/U or letter grading.

206. Modern Survey Methods (4)

Lecture, three hours. Requisites: courses 201A, 201B. Advancements in modern survey methodology. Examination of traditional approaches and consideration of cutting-edge solutions in fields of research in survey methodology. Development of students’ own research. S/U or letter grading.

C216. Social Statistics (4)

Lecture, three hours. Preparation: some knowledge of basic calculus and linear algebra. Requisites: courses 100A and 100B, or 101B and 101C, or one course from 10, 11, 12, 13 and one upper division statistics course using regression. Designed for social sciences graduate students and advanced undergraduate students seeking training in data issues and methods employed in social sciences. Concurrently scheduled with course C116. S/U or letter grading.

218. Statistical Analysis of Networks (4)

Lecture, three hours. Limited to graduate students. Introduction to analysis of social structure, conceived in terms of social relationships. Major concepts of social network theory and mathematical representation of social concepts such as role and position. Use of graphical representations of network information. S/U or letter grading.

M222. Spatial Statistics (4)

(Same as Geography M205 and Urban Planning M215.) Lecture, three hours. Designed for graduate students. Survey of modern methods used in analysis of spatial data. Implementation of various techniques using real data sets from diverse fields, including neuroimaging, geography, seismology, demography, and environmental sciences. S/U or letter grading.

M243. Logic, Causation, and Probability (4)

(Same as Epidemiology M204.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: Epidemiology 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.

C273. Applied Geostatistics (4)

Lecture, three hours; discussion, one hour. Geostatistics can be applied to many problems in other disciplines such as hydrology, traffic, air and water pollution, epidemiology, economics, geography, waste management, forestry, oceanography, meteorology, and agriculture and, in general, to every problem where data are observed at geographic locations. Acquisition of knowledge from different areas that can be used to analyze real spatial data problems and to connect geostatistics with geographic information systems (GIS). Concurrently scheduled with course C173. S/U or letter grading.

Education Courses

231A. Toolkit for Quantitative Methods Research (4)

Lecture, four hours. Requisites: courses 230A, 230B, 230C. Elementary probability. Working knowledge with calculus. Mathematical and statistical results useful for advanced quantitative methodology research. Matrix algebra. Random vectors. Multivariate distribution theory. Likelihood and Bayesian estimation and inference. Linear and generalized linear models. Simulation. S/U or letter grading.

M231B. Factor Analysis (4)

(Formerly numbered 231B.) (Same as Psychology M253.) Lecture, four hours. Requisites: courses 211B, 231A. Exploratory factor analysis, rotations, confirmatory factor analysis, multiple-group analysis. S/U or letter grading.

231BL. Factor Analysis: Computer Laboratory (1)

Laboratory, one hour. Corequisite: course 231B. Computer data analysis laboratory for exploratory and confirmatory factor analysis. Instruction in CEFA, LISREL, and other relevant statistical analysis packages. S/U grading.

231C. Analysis of Categorical and Other Nonnormal Data (4)

Lecture, four hours. Requisites: courses 230B, 230C. Regression analysis with dichotomous and polytomous dependent variables, log-linear modeling, coefficients of association for categorical variables, factor analysis, and structural equation modeling. Letter grading.

231D. Advanced Quantitative Models in Nonexperimental Research: Multilevel Analysis (4)

Lecture, four hours. Requisites: courses 230B, 230C. Examination of conceptual, substantive, and methodological issues in analyzing multilevel data (i.e., on individuals in organizational settings such as schools, corporations, hospitals, communities); consideration of alternative analytical models. Letter grading.

M231E. Statistical Analysis with Latent Variables (4)

(Same as Statistics M244.) Lecture, three hours. Requisites: courses 231A, 231B. Extends path analysis (causal modeling) by considering models with measurement errors and multiple indicators of latent variables. Confirmatory factor analysis, covariance structure modeling, and multiple-group analysis. Identification, estimation, testing, and model building considerations. Letter grading.

231EL. Latent Variable Modeling: Computer Laboratory (1)

Laboratory, one hour. Corequisite: course M231E. Computer data analysis laboratory for latent variable modeling. Instruction in LISREL and other relevant statistical analysis packages. S/U grading.

Public Policy Courses

M218. Research Design and Methods for Social Policy (4)

(Same as Urban Planning M204.) Lecture, three hours; outside study, nine hours. Limited to graduate students. How to become more sophisticated consumers and producers of qualitative and quantitative policy research. In first half of course, formal principles of research design; in second half, various data collection methods, including ethnography, interviewing, and survey design. Letter grading.

Epidemiology

203. Topics in Theoretical Epidemiology (2)
Lecture, two hours. Selected topics from current research areas in epidemiologic theory and quantitative methods. Topics selected from biologic models, epidemiologic models, problems in inference, model specification problems, design issues, analysis issues, and confounding. May be repeated for credit with consent of instructor. S/U grading.

M204. Logic, Causation, and Probability (4)
(Same as Statistics M243.) Lecture, four hours. Preparation: two terms of statistics or probability and statistics. Recommended requisite: course 200C. Principles of deductive logic and causal logic using counterfactuals. Principles of probability logic and probabilistic induction. Causal probability logic using directed acyclic graphs. S/U or letter grading.

M211. Statistical Methods for Epidemiology (4)
(Same as Statistics M250.) Lecture, four hours. Preparation: two terms of statistics (such as Biostatistics 100A, 100B). Enforced requisites: courses 200B, 200C. Concepts and methods tailored for analysis of epidemiologic data, with emphasis on tabular and graphical techniques. Expansion of topics introduced in courses 200B and 200C and introduction of new topics, including principles of epidemiologic analysis, trend analysis, smoothing and sensitivity analysis. S/U or letter grading.

212. Statistical Modeling in Epidemiology (4)
(Formerly numbered M212.) Lecture, four hours. Preparation: two terms of statistics (three terms recommended). Recommended: course M204 or M211. Principles of modeling, including meanings of models, a priori model specification, translation of models into explicit population assumptions, model selection, model diagnostics, hierarchical (multilevel) modeling. S/U or letter grading.