Do file stata diff code psmatch2 treatment control download

I don't know whether -psmach2- can be tailored to your "uncommon" purpose. Huang, that's okay. By the way, I tried to added the option -n 4 - to the syntax but the output shows there is no change of the number of treated and control groups. Any clue of why this is happening? Below is my output and if you scroll down to the very last, the total number of treated and controls stays the same to the syntax without the option -n 4 -.

Paulo Loureiro. Could someone help me? I think the update has caused problems. The Stata Journal 2, Number 4, pp. Thanks for your help. Phyu Zin. Hi Dr Huang If i have 2 different outcomes grad and income in the same study cohort, do I have to do separate matching process for each outcome measure?

Did I do the matching correctly? Thank you so much in advance. By default teffects psmatch does not add any new variables to the data set. However, there are a variety of useful variables that can be created with options and post-estimation predict commands. The following table lists the 1st and th observations of the example data set after some of these variables have been created.

We'll refer to it as we explain the commands that created the new variables. Reviewing these variables is also a good way to make sure you understand exactly how propensity score matching works. The gen option tells teffects psmatch to create a new variable or variables.

For each observation, this new variable will contain the number of the observation that observation was matched with. If there are ties or you told teffects psmatch to use multiple neighbors, then gen will need to create multiple variables. Thus you supply the stem of the variable name, and teffects psmatch will add suffixes as needed. In this case each observation is only matched with one other, so gen match only creates match1.

Referring to the example output, the match of observation 1 is observation which is why those two are listed.

Note that these observation numbers are only valid in the current sort order, so make sure you can recreate that order if needed.

If necessary, run:. The predict command with the ps option creates two variables containing the propensity scores, or that observation's predicted probability of being in either the control group or the treated group:. Observations 1 and were matched because their propensity scores are very similar.

The po option creates variables containing the potential outcomes for each observation:. Because observation 1 is in the control group, y0 contains its observed value of y.

The propensity score matching estimator assumes that if observation 1 had been in the treated group its value of y would have been that of the observation in the treated group most similar to it where "similarity" is measured by the difference in their propensity scores. Observation is in the treated group, so its value for y1 is its observed value of y while its value for y0 is the observed value of y for its match, observation The treatment effect is simply the difference between y1 and y0.

You could calculate the ATE yourself but emphatically not its standard error with:. Another way to conceptualize propensity score matching is to think of it as choosing a sample from the control group that "matches" the treatment group. Any differences between the treatment and matched control groups are then assumed to be a result of the treatment.

Note that this gives the average treatment effect on the treated—to calculate the ATE you'd create a sample of the treated group that matches the controls. Mathematically this is all equivalent to using matching to estimate what an observation's outcome would have been if it had been in the other group, as described above. Sometimes researchers then want to run regressions on the "matched sample," defined as the observations in the treated group plus the observations in the control group which were matched to them.

The problem with this approach is that the matched sample is based on propensity scores which are estimated, not known. Thus the matching scheme is an estimate as well. Running regressions after matching is essentially a two stage regression model, and the standard errors from the second stage must take the first stage into account, something standard regression commands do not do.

This is an area of ongoing research. We will discuss how to run regressions on a matched sample because it remains a popular technique, but we cannot recommend it. With option altv ariance one can specify to use the estimator of Abadie et al. Note that this is appropriate for nearest-neighbor matching on the X's, i. Mahalanobis-metric matching mahal not augmented with the propensity score. By default the sample variance is calculated population variance can be calculated using option pop ulation.

Alternatively, indepvars need to be specified to allow the program to estimate the propensity score on them. In this case:. Defaults to 1. Default matching method is single nearest-neighbour without caliper. Nearest neigbor propensity score matching only.

Nearest neighbor propensity score matching only. Use to perform nearest neighbor s within caliper, radius matching and Mahalanobis 1-to-1 matching. Default bandwidth is 0. Mahalanobis-metric matching becomes matching on a quadratic metric with the specified weighting matrix.

Default is the fourth root of the number of comparison units. The commands pstest , psgraph. Leuven and B. Abadie, A. Abadie A. Cochran, W. Dehejia, R. H and Wahba, S.