This notebook replicates code examples from Nick Huntington-Klein's book on causal inference, [The Effect](https://theeffectbook.net/).


```{python}
from causaldata import Mroz, gapminder, organ_donations, restaurant_inspections

import pyfixest as pf

%load_ext watermark
%watermark --iversions
```


## Chapter 4: Describing Relationships


```{python}
# Read in data
dt = Mroz.load_pandas().data
# Keep just working women
dt = dt.query("lfp")
# Create unlogged earnings
dt.loc[:, "earn"] = dt["lwg"].apply("exp")

# 5. Run multiple linear regression models by succesively adding controls
fit = pf.feols(fml="lwg ~ csw(inc, wc, k5)", data=dt, vcov="iid")
pf.etable(fit)
```

## Chapter 13: Regression

### Example 1


```{python}
res = restaurant_inspections.load_pandas().data
res.inspection_score = res.inspection_score.astype(float)
res.NumberofLocations = res.NumberofLocations.astype(float)
res.dtypes

fit = pf.feols(fml="inspection_score ~ NumberofLocations", data=res)
pf.etable([fit])
```


### Example 2


```{python}
df = restaurant_inspections.load_pandas().data

fit1 = pf.feols(
    fml="inspection_score ~ NumberofLocations + I(NumberofLocations^2) + Year", data=df
)
fit2 = pf.feols(fml="inspection_score ~ NumberofLocations*Weekend + Year", data=df)

pf.etable([fit1, fit2])
```



### Example 3: HC Standard Errors


```{python}
pf.feols(fml="inspection_score ~ Year + Weekend", data=df, vcov="HC3").summary()
```


### Example 4: Clustered Standard Errors


```{python}
pf.feols(
    fml="inspection_score ~ Year + Weekend", data=df, vcov={"CRV1": "Weekend"}
).tidy()
```

### Example 5: Bootstrap Inference


```{python}
fit = pf.feols(fml="inspection_score ~ Year + Weekend", data=df)
fit.wildboottest(reps=999, param="Year")
```


## Chapter 16: Fixed Effects

### Example 1

tba

### Example 2


```{python}
gm = gapminder.load_pandas().data
gm["logGDPpercap"] = gm["gdpPercap"].apply("log")

fit = pf.feols(fml="lifeExp ~ C(country) + np.log(gdpPercap)", data=gm)
fit.tidy().head()
```


### Example 3: TWFE


```{python}
# Set our individual and time (index) for our data
fit = pf.feols(fml="lifeExp ~ np.log(gdpPercap) | country + year", data=gm)
fit.summary()
```


## Chapter 18: Difference-in-Differences

### Example 1


```{python}
od = organ_donations.load_pandas().data

# Create Treatment Variable
od["California"] = od["State"] == "California"
od["After"] = od["Quarter_Num"] > 3
od["Treated"] = 1 * (od["California"] & od["After"])

did = pf.feols(fml="Rate ~ Treated | State + Quarter", data=od)
did.summary()
```


### Example 3: Dynamic Treatment Effect


```{python}
od = organ_donations.load_pandas().data

# Create Treatment Variable
od["California"] = od["State"] == "California"
# od["Quarter_Num"] = pd.Categorical(od.Quarter_Num)
od["California"] = od.California.astype(float)

did2 = pf.feols(
    fml="Rate ~ i(Quarter_Num, California,ref=3) | State + Quarter_Num", data=od
)

did2.tidy()
```