Видео

The real question is who puts milk in before the tea?!

Thanks for this video!

Really good

Thanks a lot bro 🎉

Why is exp squared added to the equation ? Please someone to explain

In this EX, are y-scores the post-scores or the pre-post differences? I`m guessing just post scores? Thanks for clarifying!

😃 What an interesting score.

Amazing Video Prof!!

Thanks for your videos. I have a problem with merge I won't to merge 1:1 sam key variable . At all I have 7 dataset I like to merge. I get error says variable _merge already defined stata. How can we solve that problem?

# Clear all rm(list = ls()) cat("\f") library(dplyr) # Needed for %>% to work # This R program follows the example given by ruclips.net/video/ACVyPp1Fy6Y/видео.html # demonstrating how to work with propensity score matching. # In the example there are 9 villages. A non governmental help organisation has built # health care centers in 4 of them where they thought it was most needed. So the treatment # "health care center" was not randomly chosen. Now we want to find the causal effect of # a new health care center on infant mortality rate. # Vectors with the data T=c(1,1,1,1,0,0,0,0,0) # Treated or not (treatment is a new health care center in the village) imrate=c(10,15,22,19,25,19,4,8,6) # infant mortality rate povrate=c(0.5, 0.6, 0.7, 0.6, 0.6, 0.5, 0.1, 0.3, 0.2) # poverty rate pcdocs=c(0.01,0.02,0.01,0.02,0.01,0.02,0.04,0.05,0.04) # pcdocs # combine the vectors into a data frame called "imdata" imdata=data.frame(T ,imrate ,povrate, pcdocs) # Do a logistic regression logistic_reg_model = glm(T ~ povrate + pcdocs , family=binomial, data=imdata) summary(logistic_reg_model) # Show the results of the logistic regression # Predict the propencity score propencity_score = predict(logistic_reg_model, type="response") imdata$PS = propencity_score # Add the propencity score to a new column in the dataframe # Create separate data frames for treated and control treated = imdata %>% filter(imdata$T==1) control = imdata %>% filter(imdata$T==0) paste("Average imrate in treatment group", mean(treated$imrate)) paste("Average imrate in control group", mean(control$imrate)) paste("Effect on imrate if treatment is compared to control", mean(treated$imrate)-mean(control$imrate)) # Last line show print 4.1, meaning that a health care center should increase morality rate by 4.1. # This is of course wrong, we are not estimating the causal effect because we are not # taken the pre-treatment characteristics into account. # Now instead create a new control group consisting of villages with as similar pre-treatment # characteristics as possible by using the method of precocity score matching. # Now do the matching in a "manual way" by looping though the treated villages and then find the # row with the closest propensity score among the not treated villages. for ( i in 1:nrow(treated) ) { min=Inf # set the minimum to positive infinity for ( j in 1:nrow(control) ) { if ( abs(treated[i, "PS"] - control[j, "PS"]) < min ) { # We have now found a row with less difference in propensity score, so # update the match to this row min=abs(treated[i, "PS"] - control[j, "PS"]) treated[i, "match"]=nrow(treated)+j } } } treated # List the data so we can see the matching # Finally calculate the average infant mortality rate among the treated # and compare it with the average infant mortality rate among the matched control villages imrate_sum_treated=0 imrate_sum_matched_control=0 for ( i in 1:nrow(treated) ) { imrate_sum_treated=imrate_sum_treated+treated[i,"imrate"] imrate_sum_matched_control=imrate_sum_matched_control + imdata[ treated[i,"match"] ,"imrate"] } avg_imrate_treated=imrate_sum_treated/nrow(treated) avg_matched_control=imrate_sum_matched_control/nrow(treated) diff_treated_vs_matched_control=avg_imrate_treated-avg_matched_control paste("avg_imrate_treated =",avg_imrate_treated) paste("avg_matched_control =",avg_matched_control) # should now display -7, meaning that the effect of building a health care center i # a reduction of 7 in infant mortality rate paste("diff_treated_vs_matched_control =",diff_treated_vs_matched_control)

Why does every tutorial I look up for this test not actually compute the answer. I don't know what "n choose m" means (or whatever you're saying). Plug in the values and show me the math!

mi problem is: how I do interpretate the new dataset generated after PSM? how do I create a table showing percentages of each categorical covariate I've chosen for matching?

How did you summarize the infant mortality rate lowering 7 deaths per 1000?was 1000 your sample population among treated and non treated infants??

This is great! Thank you so much

wish me luck boys, tomorrow final boss of my educational career, exam in econometrics. thanks a lot mr. McKee, your videos are so helpful in getting that helicopter view of these concepts. Good luck to anyone still struggeling, we're all gonna make it!

Great explanation!

Interesting (y)

Excellent video, thank you

Thanks , it is well explained

way more intuative than previously thought, well put thanks

Which program do you use to calculate this analysis? Are there some code packages, which can be used and upload data? Thanks!

8 years later and you are still saving lives. Thank you, sir.

Thank you! I would like to know, if there isn't a comparable group, like Rio, then how can one figure out the effect of this programme?

God, this is so good!

Awesome!

eh

why are you considering weights when calculating effect size. eg 0.25*() - 0.25*() - where did this 0.25 came from and why?

I'm speechless how a video of 20 min explained to me everything I was struggling with for a half of year Thank you a lot for making it so simple and clear. I would love more teachers to explain the basics like this.

Okay the second time watching this I finally understood. Thank you!

It sucks being stupid because I understood nothing

Thanks for the video! It is very clear, just a quick question: how did you compute in Stata the column "ps1"?

Its really helpful, but can you please tell how you calculated ps1? How can I do it in Stata?

At 16:11, can we also say that someone with a low ses, is 4.84 time more likely to like ice cream? Meaning if this data was regressed for *only* dummy variable ses=1, would that be the odds ratio?

decent vid

Amazing explanation of 2sls

Great video

thanks for important efforts

Excellent video, thank you very much! Can you maybe quickly explain how you calculated and displayed PS1 in Stata? I understand how to run the regression but I struggle to find the PS1 outputs per line, so I can actually match one line to another

This is excellent

amazingly explained! Thanks

26:30 "being female seems to have the odds of preferring chocolate to strawberry relative to male, but it's not significantly different from no effect at all". Are you suggesting that being female increases the odds of picking the chocolate flavour relative to being male? I think that it's the opposite here.

11:54 "that's the predicted probability...". Shouldn't it be a "odds" rather than "probability"?

great explanation

Sorry, hard to follow

where is the 0.25 in the equation coming from not ?

This is really helpful, thank you! :)

Thank you for this! I didn't quite understand the very last point, i.e. the difference between the points made for when DD is 'ok' (appropriate) and 'not ok'

Among the dozens of PSM videos, this stands out as simply the best. The central example, shown clearly with the intuitive elements highlighted, and the discussion at the end regarding what PSM does *not* do- are crucial and critical! One suggestion: insert a slide showing the logit regression model to really highlight where the probabilities are coming from.

Thank you!

Loads of thanks for such simple explanation