There are two types of Intervention studies . The first one is called Intervention Analysis (de jure )..the second is called Intervention Detection (de facto). Simply search here for R and one or the other.
The ultimate approach is to use a SARMAX model https://autobox.com/pdfs/SARMAX.pdf to form a useful equation leading directly to tests of statistical significance.
Note that there are two type of Intervention Variables , de jure and de facto . If you know the date and the type of Intervention then you are fundamentally specifying a de jure ( by law/supposition ) "X" variable. If you don't know ( or are not sure ) the date and type of intervention (de facto ...by fact ) then one needs to identify an "I" type variable using schemes following http://docplayer.net/12080848-Outliers-level-shifts-and-variance-changes-in-time-series.html.
In either case the X and the I appear/act as possible predictors in the final model possibly including ARIMA structure.
EDITED AFTER RECEIPT & ANALYSIS OF COUNTRY1'S DATA:
When forming a useful time series model (SARMAX) one needs to consider the following three components:
type 1. The contemporaneous and lag effects of known user-suggested predictor series . The are the X series.
type 2. The impact of unknown stochastic series whose impact can be proxied by the history of Y .This is the arima component .
type 3. The impact of unknown deterministic series whose impact can be proxied by empirically identified latent deterministic structure (pulses , level/step shifts, seasonal pulses , deterministic time trends).These are called I series .
One needs to efficiently combine three components by examining alternative scenarios/model and selecting the one that is minimally sufficient which is suggestd here http://www.autobox.com/pdfs/TRANSFER%20FUNCTION%20FLOW%20CHART.docx.
Attempting identify type 1 structure using ordinary regression techniques is not robust.
Attempting to identify type 2 effects (arima structure) in the presence of either type 1 or type 3 effects is not robust.
Attempting to identify type 3 effects via Intervention Detection procedures which assume that type 1 and type 2 are both nul is not robust.
What is required is a holistic approach /self-checking / self-improving sequence of heuristics which examine feasible combinations in a step-up and step-down manner culminating in a "possibly useful model".
There are two predictor series (POP and GDP ) for 24 consecutive years .
AUTOBOX ( a time series package that I have helped to develop ) was used to identify BOTH the regression effects for the two predictors AND any needed arima structure AND empirically identify any latent deterministic structure reflecting omitted variables such as law changes .
I am not an expert in the software you referenced but I don't believe it allows the inclusion of causals and their lags or an ARIMA structure as it goes about the business of identifying pulses and/or level/step shifts.
Here are the results and here
The coefficients in the model present the effects that you have asked for.
Here is a plot of the Actual, Fitted and Forecast using the most recent years values for the two predictors sowing one pulse and two level/step shifts . Note that level/step shifts are intercept changes.
Here is the Cleansed Graph showing what occurred and what would have occurred had there been no level/step shifts.
In summary all software has limitations ... you just need to know what they are and not simply just press a button because very soon there will be a button to replace you !.
I hope this helps you and others forming models that may or may not have user-specified variables and may or may not have needed arima structure, both of which can't be ignored when identifying omitted deterministic structure.