There are a large number of resources within Stata for the estimation of treatment effects. Some are part of official Stata and others are user written that can be easily downloaded.
To estimate regression discontinuity models, there is a download rd due to Austin Nichols. Further details at Nichols, Austin. 2007. "Causal Inference with Observational Data." Prepublication draft available at http://pped.org/stata/ciwod.pdf. It is published in the Stata Journal now I think.
To estimate IV models there are several options in Stata.
ivregress is the main program. A download ivreg2 due to Baum, Schaffer & Stillman is very useful - I recommend it. Make sure you get the latest version. Their paper should be used in conjunction with it: http://ideas.repec.org/a/tsj/stataj/v7y2007i4p465-506.html. xtivreg2 is the equivalent program for panel data.
ivtobit and ivprobit do what their names suggest. If using them, you need to satisfy yourself that they are consistent estimators. Caution is appropriate where the instrumented variable is binary. In the latter case biprobit may be better.
cmp (due to David Roodman) allows you to estimate using MLE a wide range of simultaneous models with combinations of linear and non-linear equations provide they satisfy a recursive structure.
treatreg allows the estimation of what Stata calls "treatment effects models". This is something of a misnomer since it only for a very specific model: a linear regression with an endogenous dummy.
condivreg estimates IV models with a single endogenous variable and provides an exact confidence interval for the slope as opposed to the usual asymptotic one. It is particularly useful if weak instruments are a concern.
For estimating Treatment effects using Propensity Score matching there are several downloads including: psmatch2 (Leuven & Sianesi) which does a wide range of matching estimators and nnmatch which does nearest neighbour matching. psbalance allows you to test covariate balance after matching - something that is recomended.