Matching Methods for Causal Inference with Time-Series Cross-Sectional Data
build_maha_mats Builds the matrices that we will then use to calculate...
build_ps_data
calculate_estimates
calculate_placebo_estimates
calculate_point_estimates Helper function that calculates the point es...
check_time_data
clean_leads Function to check the lead windows in treated and control ...
Visualize the treatment distribution across units and time in a panel ...
Extract the distances of matched control units
Get distances See distances.matched.set method
enforce_lead_restrictions check treatment and control units for treatm...
equality_four_placebo
equality_four Small helper function implementing estimation function f...
expand_treated_ts Builds a list that contains all times in a lag windo...
extract_differences This function calculates the differences from t-1 ...
Extract matched.set objects from PanelMatch results
Extract matched.set objects from PanelMatch results
find_ps
findBinaryTreated
Calculate covariate balance measures for refined and unrefined matched...
Calculate matched set level treatment effects
Extract unrefined covariate balance results, if they exist
Extract just the unrefined covariate balance results, if they exist
get.matchedsets
getDits returns a vector of Dit values, as defined in the paper. They ...
getWits returns a vector of Wits, as defined in the paper (equation 25...
handle_bootstrap_parallel
handle_bootstrap_placebo_parallel
handle_bootstrap_placebo
handle_bootstrap
handle_conditional_se Calculates conditional standard errors analytica...
handle_mahalanobis_calculations Returns a matched.set object with weig...
handle_missing_data
handle_moderating_variable
handle_ps_match Returns a matched.set object with weights for control ...
handle_ps_weighted
handle_conditional_se Calculates conditional standard errors analytica...
identifyDirectionalChanges Identifies changes in treatment variable fo...
lwd_refinement master function that performs refinement with listwise ...
lwd_units helper function that actually subsets sets down to contain u...
A constructor for the matched.set class.
merge_formula
Pre-process and balance panel data
Estimate a causal quantity of interest
Matching Methods for Causal Inference with Time-Series Cross-Sectional...
Create and refine sets of matched treated and control observations
parse_and_prep
Prepare Control Units pcs and pts create data frames with the time/id ...
perform_refinement Performs refinement of matched sets, ultimately ret...
perunitSum_Dit Similar to perunitSum, this is a low level helper funct...
perunitSum This is a low level function that is used to calculate a va...
Conduct a placebo test
Helper function for plotting the distribution of matched set sizes
Plot the distribution of control unit weights
Plot covariate balance results Create figures displaying covariate bal...
Create basic plots of PanelData objects
Plot point estimates and standard errors from a PanelEstimate calculat...
Plot the distribution of the sizes of matched sets.
prepare_data The calculation of point estimates and standard errors fi...
Print matched.set objects.
Print basic information about PanelBalance objects
Print PanelData objects and basic metadata
Print point estimates and standard errors
Print PanelMatch objects.
set_lwd_refinement Performs the set-level operations for refinement wi...
Subset matched.set object
Subset PanelBalance objects
Summarize information about a matched.set object and the matched sets ...
Summarize covariate balance over time
Summarize the number of unique units and time periods in a PanelData o...
Get summaries of PanelEstimate objects and calculations
Summarize information about a PanelMatch object and the matched sets c...
Extract the weights of matched control units
Get weights of matched control units See weights.matched.set method
Implements a set of methodological tools that enable researchers to apply matching methods to time-series cross-sectional data. Imai, Kim, and Wang (2023) <http://web.mit.edu/insong/www/pdf/tscs.pdf> proposes a nonparametric generalization of the difference-in-differences estimator, which does not rely on the linearity assumption as often done in practice. Researchers first select a method of matching each treated observation for a given unit in a particular time period with control observations from other units in the same time period that have a similar treatment and covariate history. These methods include standard matching methods based on propensity score and Mahalanobis distance, as well as weighting methods. Once matching and refinement is done, treatment effects can be estimated with standard errors. The package also offers diagnostics for researchers to assess the quality of their results.