README.qmd

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format: gfm
jupyter: python3
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# TreatmentEffectRisk

This repository is an implementation of the Treatment Effect Risk: Bounds and Inference package, based on the replication by Kallus (2024).

# References

- Paper: [Treatment Effect Risk: Bounds and Inference](https://arxiv.org/abs/2201.05893)
- Original Repository: [CausalML/TreatmentEffectRisk](https://github.com/CausalML/TreatmentEffectRisk)

# License

This project is licensed under the MIT License - see the LICENSE file for details.

<!-- ## MAP

![](./original_pkg/functions.png) -->

# Contact 

For questions or inquiries about the package, please contact [fr.jhonk@gmail.com].

# Usage

```{python}
# pip install TrER==0.1.41
```

```{python}
import numpy as np
import pandas as pd
url_csv = 'https://raw.githubusercontent.com/CausalML/TreatmentEffectRisk/main/data/behaghel.csv'
import warnings 

warnings.filterwarnings("ignore")
```



```{python}

job = pd.read_csv(url_csv)


Xbin = [
    'College_education', 'nivetude2', 'Vocational', 'High_school_dropout', 
    'Manager', 'Technician', 'Skilled_clerical_worker', 'Unskilled_clerical_worker', 
    'Skilled_blue_colar', 'Unskilled_blue_colar', 'Woman', 'Married', 'French', 
    'African', 'Other_Nationality', 'Paris_region', 'North', 'Other_regions', 
    'Employment_component_level_1', 'Employment_component_level_2', 
    'Employment_component_missing', 'Economic_Layoff', 'Personnal_Layoff', 
    'End_of_Fixed_Term_Contract', 'End_of_Temporary_Work', 'Other_reasons_of_unemployment', 
    'Statistical_risk_level_2', 'Statistical_risk_level_3', 'Other_Statistical_risk', 
    'Search_for_a_full_time_position', 'Sensitive_suburban_area', 'Insertion', 
    'Interim', 'Conseil'
]

Xnum = [
    'age', 'Number_of_children', 'exper', 'salaire.num', 'mois_saisie_occ', 'ndem'
]

Xall = Xbin + Xnum


job_binary = job[(job['A_public'] == 1) | (job['A_private'] == 1)].copy()
job_binary['sw'] = job_binary['sw'] / job_binary['sw'].mean()
job_binary['A'] = job_binary['A_public']
job_binary['ipw'] = 1 / (
    job_binary['A_standard'] * job_binary['sw'] * job_binary['A_standard'].mean() + 
    job_binary['A_private'] * job_binary['sw'] * job_binary['A_private'].mean() + 
    job_binary['A_public'] * job_binary['sw'] * job_binary['A_public'].mean()
)
```

## Imports



```{python}
from TrER.utils import make_cvgroup_balanced
from TrER.models import tau_predict, mu_calculate, var_calculate
from TrER.plots import plot_cvar_group, plot_cvar_groups_with_markers
from TrER.plots import plot_cvar, plot_CVAR_TE
from TrER.gen_cvar import cvar_tau, cvar_plugin, cvar_mate, cvar_bbound_mate, cvar_bbounded, prep_bbounds_ate, job_condvar_gen, cvar_bbounds_ate
```

## Pre-processing

```{python}
ps = np.arange(0.01, 1.01, 0.01)
bs = np.arange(0, 0.30, 0.05)
rhos = [-1, -0.5, 0, 0.5, 0.9, 0.95, 1]
np.random.seed(0)
K = 5
cvgroup = make_cvgroup_balanced(job_binary, K, 'A')
job_binary['y_ref'] = (2 * job_binary['A'] - 1) * job_binary['ipw'] * job_binary['Y']

# X and y for the model
X = job_binary[Xall]  # Assuming Xall is a list of column names
y = job_binary['y_ref']

tau_pred = tau_predict(X, y, job_binary['sw'])

bad_c = ['age','Paris_region','African','High_school_dropout']
Xbad = job_binary[bad_c]
tau_pred_bad = tau_predict(Xbad, y, job_binary['sw'])
```



## Estimation

```{python}

y = job_binary['Y']
X = job_binary[Xall]
mu0, mu1 = mu_calculate(
    job_binary, cvgroup, y, X 
)
```

```{python}
job_binary['mu0'] = mu0
job_binary['mu1'] = mu1
var0, var1 = var_calculate(job_binary, job_binary[Xall], cvgroup)
job_binary['var0'] = var0
job_binary['var1'] = var1
job_binary['tau'] = tau_pred
job_binary['tau_bad'] = tau_pred_bad
```

## Calculations and plotting



```{python}
CVaR = cvar_tau(job_binary, ps)
plot_cvar(CVaR, rearrangement=False)
plot_cvar(CVaR, rearrangement=True)
```

```{python}
cvar_p = cvar_plugin(job_binary, ps)
plot_cvar(cvar_p)
```

```{python}
cvar_bad = cvar_tau(job_binary, ps, tau_col='tau_bad')
plot_cvar(cvar_bad, rearrangement=True)
```

```{python}
cvarmate = cvar_mate(job_binary, ps)
plot_cvar (cvarmate , rearrangement= True)
```

```{python}
cvar_bbound_mate_df = cvar_bbound_mate(job_binary, ps, bs)
plot_cvar_group(cvar_bbound_mate_df)
```

```{python}
df_bounded = cvar_bbounded(cvarmate, cvar_bbound_mate_df, bs)
plot_cvar_groups_with_markers(
    df_bounded, "p", y = "CVaR"
)
```

```{python}
df_1, tot_var, cnd_1, cnd_2 = prep_bbounds_ate(job_binary)
sbound_mate = cvar_bbounds_ate (df_1, ps, rhos, tot_var)
```

```{python}
plot_cvar_group(
    sbound_mate.query("p>0.25"),
    group="rho",
    y_label=r"${CVaR}_{\alpha}(\tau(X))-\bar\tau$",
)
```

```{python}
job_condvar = job_condvar_gen(cvarmate, sbound_mate, cnd_1, cnd_2)
plot_cvar_groups_with_markers(
    job_condvar,
    "p",
    "CVaR",
    main_group="rho",
    cvar_se="CVaR_se",
    y_label=r"${CVaR}_{\alpha}-\bar{\tau}$",
)
```

```{python}
plot_CVAR_TE(job_binary, ps)
```

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