Matching and Weighting for Causal Inference with R - Online Course
A 4-Day Livestream Seminar Taught by
Stephen VaiseyTuesday, June 18 –
Friday, June 21, 2024
10:30am-12:30pm (convert to your local time)
1:30pm-3:00pm
This course offers an in-depth introduction to matching and weighting methods using R. Researchers use matching and weighting to identify the causal effect of a treatment on an outcome — such as the effect of smoking on health or the effect of divorce on child outcomes — when assignment to the treatment is not random. Many of these techniques rely on traditional propensity scores but the course will also cover newer techniques that are not propensity-score based.
Matching and weighting have two notable advantages over standard regression methods. First, they are less dependent on correct model specification. Second, they can easily produce separate estimates of causal effects for subjects who are likely to receive the treatment and for those who are unlikely to receive it, a distinction that is especially important for policy work.
Starting June 18, we are offering this seminar as a 4-day synchronous*, livestream workshop held via the free video-conferencing software Zoom. Each day will consist of two lecture sessions which include hands-on exercises, separated by a 1-hour break. You are encouraged to join the lecture live, but will have the opportunity to view the recorded session later in the day if you are unable to attend at the scheduled time.
*We understand that finding time to participate in livestream courses can be difficult. If you prefer, you may take all or part of the course asynchronously. The video recordings will be made available within 24 hours of each session and will be accessible for four weeks after the seminar, meaning that you will get all of the class content and discussions even if you cannot participate synchronously.
Closed captioning is available for all live and recorded sessions. Live captions can be translated to a variety of languages including Spanish, Korean, and Italian. For more information, click here.
More details about the course content
This seminar is both conceptual and practical. It will briefly introduce directed acyclic graphs (DAGs) and the potential outcomes framework to motivate matching and weighting practices. We will also discuss the conceptual differences between types of effects, including average treatment effects (ATEs) and average treatment effects on the treated (ATTs). The course will then guide you from simple exact matching and “traditional” propensity score approaches to more recent developments like covariate balancing propensity scores and entropy balancing. It will also consider how to integrate matching with regression to create “doubly robust” estimates of causal effects. Participants will get practical experience by working through exercises from the social and health sciences.
This seminar is both conceptual and practical. It will briefly introduce directed acyclic graphs (DAGs) and the potential outcomes framework to motivate matching and weighting practices. We will also discuss the conceptual differences between types of effects, including average treatment effects (ATEs) and average treatment effects on the treated (ATTs). The course will then guide you from simple exact matching and “traditional” propensity score approaches to more recent developments like covariate balancing propensity scores and entropy balancing. It will also consider how to integrate matching with regression to create “doubly robust” estimates of causal effects. Participants will get practical experience by working through exercises from the social and health sciences.
Computing
The instructor will use R with RStudio to demonstrate the techniques. The course relies heavily on the MatchIt and WeightIt packages, which can be downloaded through R. To participate in the hands-on exercises, you are strongly encouraged to have your computer available with R and RStudio already installed.
Basic familiarity with R is highly desirable. If you are new to R, check out Professor Vaisey’s one-hour Introduction to R video to get up to speed. You may want to consider taking a short introductory seminar on R. But even novice R coders will be able to follow the lectures and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
The instructor will use R with RStudio to demonstrate the techniques. The course relies heavily on the MatchIt and WeightIt packages, which can be downloaded through R. To participate in the hands-on exercises, you are strongly encouraged to have your computer available with R and RStudio already installed.
Basic familiarity with R is highly desirable. If you are new to R, check out Professor Vaisey’s one-hour Introduction to R video to get up to speed. You may want to consider taking a short introductory seminar on R. But even novice R coders will be able to follow the lectures and do the exercises.
If you’d like to take this course but are concerned that you don’t know enough R, there are excellent on-line resources for learning the basics. Here are our recommendations.
Who should register?
This course is for anyone who wants to learn to apply matching and weighting to observational data to improve their causal inferences. You should have a basic foundation in linear and logistic regression.
This course is for anyone who wants to learn to apply matching and weighting to observational data to improve their causal inferences. You should have a basic foundation in linear and logistic regression.
Seminar outline
- Theoretical background
- The experimental ideal
- The potential outcomes framework
- Defining different treatment effects
- Conditional independence assumption
- Exact matching
- The goal of exact matching
- Implementation in MatchIt
- Getting treatment effect estimates with WLS
- Feasible estimates
- Propensity-score methods
- Theory and estimation
- Propensity-score stratification in MatchIt
- Propensity-score matching in MatchIt
- Assessing covariate balance
- Mean differences
- Kolmogorov-Smirnov distances
- Using cobalt for plotting balance diagnostics
- Matching variations: replacement, calipers, etc.
- Bootstrapping for standard errors
- Propensity-score weighting
- IPTW using WeightIt
- Covariate balancing propensity scores using WeightIt
- Non-parametric methods
- Mahalanobis distance matching in MatchIt
- Entropy balancing in WeightIt
- Parametric regression with preprocessed data
- Double robustness property
- Estimation with MatchIt/WeightIt + lm
- Advanced topics (briefly as time permits)
- Multinomial treatments in WeightIt
- Continuous treatments in WeightIt
- Synthetic control method (difference-in-differences + weighting)
- Theoretical background
- The experimental ideal
- The potential outcomes framework
- Defining different treatment effects
- Conditional independence assumption
- Exact matching
- The goal of exact matching
- Implementation in MatchIt
- Getting treatment effect estimates with WLS
- Feasible estimates
- Propensity-score methods
- Theory and estimation
- Propensity-score stratification in MatchIt
- Propensity-score matching in MatchIt
- Assessing covariate balance
- Mean differences
- Kolmogorov-Smirnov distances
- Using cobalt for plotting balance diagnostics
- Matching variations: replacement, calipers, etc.
- Bootstrapping for standard errors
- Propensity-score weighting
- IPTW using WeightIt
- Covariate balancing propensity scores using WeightIt
- Non-parametric methods
- Mahalanobis distance matching in MatchIt
- Entropy balancing in WeightIt
- Parametric regression with preprocessed data
- Double robustness property
- Estimation with MatchIt/WeightIt + lm
- Advanced topics (briefly as time permits)
- Multinomial treatments in WeightIt
- Continuous treatments in WeightIt
- Synthetic control method (difference-in-differences + weighting)
Payment information
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.
The fee of $995 includes all course materials.
PayPal and all major credit cards are accepted.
Our Tax ID number is 26-4576270.