Individual Treatment Effects

[PrannoyLFC]

Hi @kbattocchi ,

I used the following code to calculate the ATE for my panel data (around ~$18).

dml = DynamicDML(model_y=outcome_model, model_t=treatment_model, cv=2, discrete_treatment=True)

dml.fit(Y=Y, T=T, X=None, W=X, groups=groups)

Is it possible to get individual treatment effects too? Ie how much causal impact the treatment would gave in the outcome variable for each row of my data?

[ellpri]

@PrannoyLFC I think, in order to get the effect for each row, you need to use dml.effect(x_test)Reference

[TimCosemans]

Hi!

I was wondering the same thing. The '.effect' method does not seem to be doing what I want. As a consequence, I have come up with this piece of code

`

def calculate_tau(df, model, X_names, T_names):

    m = len(T_names)

    coefficients = [model.coef_[i::m] for i in range(0, m)]
    # coeffcients for each treatment in shape (m, t, P)
    coefficients = np.stack(coefficients, axis=0)

    intercept = [model.intercept_[i::m] for i in range(0, m)]
    # intercept for each treatment in shape (m, t, 1)
    intercept = np.stack(intercept, axis=0)[:, :, np.newaxis]

    # only the first row for heterogeneity
    X = df.loc[:, X_names].head(1).to_numpy()

    # DML estimators assume that the treatment effect is linear in T (assuming there is no treatment featurizer)
    # marginal effect is constant regardless of what values of T you provide
    # If either argument is N-D, N > 2, it is treated as a stack of matrices
    # residing in the last two indexes and broadcast accordingly
    effects = np.add(np.matmul(coefficients, X.T), intercept)
    effects = np.squeeze(effects, axis=2)  # shape (m, t)

    T = df.loc[:, T_names].to_numpy()

    df["tau"] = np.sum(np.multiply(effects.T, T), axis=1)  # shape (t)

    return df

data = data.sort_values(["ID", "time"]).groupby("ID").apply(
    calculate_tau, model=est, X_names=X.columns, T_names=T.columns).reset_index(drop=True)

`