inference: Given a test market, analyze the impact of an intervention

In klarsen1/MarketMatching: Market Matching and Causal Impact Inference

Given a test market, analyze the impact of an intervention

Description

inference Analyzes the causal impact of an intervention using the CausalImpact package, given a test market and a matched_market object from the best_matches function. The function returns an object of type "market_inference" which contains the estimated impact of the intervention (absolute and relative).

Usage

inference(matched_markets=NULL,
          bsts_modelargs=NULL,
          test_market=NULL,
          end_post_period=NULL,
          alpha=0.05,
          prior_level_sd=0.01,
          control_matches=5, 
          analyze_betas=FALSE, 
          nseasons=NULL)

Arguments

matched_markets	A matched_market object created by the market_matching function
bsts_modelargs	A list() that passes model parameters directly to bsts – such as list(niter = 1000, nseasons = 52, prior.level.sd=0.1) This parameter will overwrite the values specified in prior_level_sd and nseasons. ONLY use this if you're using intricate bsts settings For most use-cases, using the prior_level_sd and nseasons parameters should be sufficient
test_market	The name of the test market (character)
end_post_period	The end date of the post period. Must be a character of format "YYYY-MM-DD" – e.g., "2015-11-01"
alpha	Desired tail-area probability for posterior intervals. For example, 0.05 yields 0.95 intervals
prior_level_sd	Prior SD for the local level term (Gaussian random walk). Default is 0.01. The bigger this number is, the more wiggliness is allowed for the local level term. Note that more wiggly local level terms also translate into larger posterior intervals This parameter will be overwritten if you're using the bsts_modelargs parameter
control_matches	Number of matching control markets to use in the analysis (default is 5)
analyze_betas	Controls whether to test the model under a variety of different values for prior_level_sd.
nseasons	Seasonality for the bsts model – e.g., 52 for weekly seasonality

Value

Returns an object of type inference. The object has the following elements:

AbsoluteEffect	The estimated absolute effect of the intervention
AbsoluteEffectLower	The lower limit of the estimated absolute effect of the intervention. This is based on the posterior interval of the counterfactual predictions. The width of the interval is determined by the alpha parameter.
AbsoluteEffectUpper	The upper limit of the estimated absolute effect of the intervention. This is based on the posterior interval of the counterfactual predictions. The width of the interval is determined by the alpha parameter.
RelativeEffectLower	Same as the above, just for relative (percentage) effects
RelativeEffectUpper	Same as the above, just for relative (percentage) effects
TailProb	Posterior probability of a non-zero effect
PrePeriodMAPE	Pre-intervention period MAPE
DW	Durbin-Watson statistic. Should be close to 2.
PlotActualVersusExpected	Plot of actual versus expected using ggplot2
PlotCumulativeEffect	Plot of the cumulative effect using ggplot2
PlotPointEffect	Plot of the pointwise effect using ggplot2
PlotActuals	Plot of the actual values for the test and control markets using ggplot2
PlotPriorLevelSdAnalysis	Plot of DW and MAPE for different values of the local level SE using ggplot2
PlotLocalLevel	Plot of the local level term using ggplot2
TestData	A data.frame with the test market data
ControlData	A data.frame with the data for the control markets
PlotResiduals	Plot of the residuals using ggplot2
TestName	The name of the test market
TestName	The name of the control market
zooData	A zoo time series object with the test and control data
Predictions	Actual versus predicted values
CausalImpactObject	The CausalImpact object created
Coefficients	The average posterior coefficients

Examples

## Not run: 
library(MarketMatching)
##-----------------------------------------------------------------------
## Analyze causal impact of a made-up weather intervention in Copenhagen
## Since this is weather data it is a not a very meaningful example. 
## This is merely to demonstrate the function.
##-----------------------------------------------------------------------
data(weather, package="MarketMatching")
mm <- best_matches(data=weather, 
                   id="Area",
                   markets_to_be_matched=c("CPH", "SFO"),
                   date_variable="Date",
                   matching_variable="Mean_TemperatureF",
                   parallel=FALSE,
                   warping_limit=1, # warping limit=1
                   dtw_emphasis=0, # rely only on dtw for pre-screening
                   matches=5, # request 5 matches
                   start_match_period="2014-01-01",
                   end_match_period="2014-10-01")
library(CausalImpact)
results <- inference(matched_markets=mm,
                     test_market="CPH",
                     analyze_betas=FALSE,
                     control_matches=5, # use all 5 matches for inference
                     end_post_period="2015-12-15",
                     prior_level_sd=0.002)

## End(Not run)

klarsen1/MarketMatching documentation built on Feb. 22, 2024, 4:11 p.m.