Errata

Firstly, it appears that the cutoff value used in the code is 10k, while it should actually be 5k. This has a downstream effect on the regression models and, consequently, the calculated LATE.

Secondly, the code implies that the ITTE can be directly derived from the conditional coefficient for the intercept in the linear regression. This would be a valid approach if the cutoff were at 0, but it's actually at 5k. This simplification seems to contradict the locality assumption of RDD, stating that the estimator is valid only near the threshold `R=c`.

Note from the Author or Editor:
In the section Intention to Treat Effect (pg 356 of the printed book), the paragraph right after the table should be updated to:


"Then, let's center the running variable, balance, to shift the threshold to zero. In this case, since the discontinuity is at 5000, you can do this by subtracting 5000 from the balance variable. (This is just a trick to make interpreting the regression parameters easier). Next, you need to regress the outcome variable on the centered running variable R interacted with a dummy for being above the threshold (R > 0):


y_i = \beta_0 + \beta_1 r_i + \beta_2 \mathbb{1}\{r_i>0\} + \beta_3 \mathbb{1}\{r_i>0\} r_i


The parameter estimate associated with crossing the threshold…"




Also, code block 20 should be:


m = smf.ols(f"pv~balance*I(balance>0)",
df_dd.assign(balance=lambda d: d["balance"]-5000)).fit()


m.summary().tables[1]


And table resulting from this code should be as in the updated code, cell 25:
https://github.com/matheusfacure/causal-inference-in-python-code/blob/main/causal-inference-in-python/11-Non-Compliance-and-Instruments.ipynb




In the section The IV Estimate, code block 21 should be updated to


def rdd_iv(data, y, t, r, cutoff):

centered_df = data.assign(**{r: data[r]-cutoff})

compliance = smf.ols(f"{t}~{r}*I({r}>0)", centered_df).fit()
itte = smf.ols(f"{y}~{r}*I({r}>0)", centered_df).fit()

param = f"I({r} > 0)[T.True]"
return itte.params[param]/compliance.params[param]




rdd_iv(df_dd, y="pv", t="prime_card", r="balance", cutoff=5000)




The result from this code block should also be updated to 732.8534752298891. See code block 27 in the GitHub link above.


Finally, the array just before the Bunching section should be updated to array([655.08214249, 807.83207567]). See code block 30 in the GitHub link above.