# 4. Heterogeneous treatment effects#

Most implemented solutions focus on the IRM or IIVM models, as for the PLR and PLIV models heterogeneous treatment effects can be usually modelled via feature construction.

## 4.1. Group average treatment effects (GATEs)#

The DoubleMLIRM and DoubleMLPLR classes contain the gate() method, which enables the estimation and construction of confidence intervals for GATEs after fitting the DoubleML object. To estimate GATEs, the user has to specify a pandas DataFrame containing the groups (dummy coded or one column with strings). This will construct and fit a DoubleMLBLP object. Confidence intervals can then be constructed via the confint() method. Jointly valid confidence intervals will be based on a gaussian multiplier bootstrap.

### 4.1.1. GATEs for IRM models#

Group Average Treatment Effects (GATEs) for DoubleMLIRM models consider the target parameters

$\theta_{0,k} = \mathbb{E}[Y(1) - Y(0)| G_k],\quad k=1,\dots, K.$

where $$G_k$$ denotes a group indicator and $$Y(d)$$ the potential outcome with $$d \in \{0, 1\}$$.

Point estimates and confidence intervals can be obtained via the gate() and confint() methods. Remark that for straightforward interpretation, the groups have to be mutually exclusive.

In [1]: import numpy as np

In [2]: import pandas as pd

In [3]: import doubleml as dml

In [4]: from doubleml.datasets import make_irm_data

In [5]: from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

In [6]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [7]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [8]: np.random.seed(3333)

In [9]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [10]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [11]: dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)

In [12]: _ = dml_irm_obj.fit()

# define groups
In [13]: np.random.seed(42)

In [14]: groups = pd.DataFrame(np.random.choice(3, 500), columns=['Group'], dtype=str)

Group
0     2
1     0
2     2
3     2
4     0

In [16]: gate_obj = dml_irm_obj.gate(groups=groups)

In [17]: ci = gate_obj.confint()

In [18]: print(ci)
2.5 %    effect    97.5 %
Group_0  0.286027  0.691136  1.096245
Group_1 -4.954536 -1.107872  2.738793
Group_2  0.135755  0.571707  1.007659


A more detailed notebook on GATEs with DoubleMLIRM models is available in the example gallery.

### 4.1.2. GATEs for PLR models#

Group Average Treatment Effects (GATEs) for DoubleMLPLR models consider a slightly adjusted version of the DoubleMLPLR model. Instead of considering a constant treatment effect $$\theta_0$$ for all observations, the adjusted model allows for a different effect based on groups.

\begin{align}\begin{aligned}Y = D \theta_0(G_k) + g_0(X) + \zeta, & &\mathbb{E}(\zeta | D,X) = 0,\\D = m_0(X) + V, & &\mathbb{E}(V | X) = 0,\end{aligned}\end{align}

where $$G_k$$ for $$k=1,\dots, K$$ denotes a group indicator where the groups can depend on the counfounding features $$X$$.

Point estimates and confidence intervals can be obtained via the gate() and confint() methods. Remark that for straightforward interpretation, the groups have to be mutually exclusive.

In [19]: import numpy as np

In [20]: import pandas as pd

In [21]: import doubleml as dml

In [22]: from doubleml.datasets import make_plr_CCDDHNR2018

In [23]: from sklearn.ensemble import RandomForestRegressor

In [24]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [25]: ml_m = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [26]: np.random.seed(3333)

In [27]: dml_data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20)

In [28]: dml_plr_obj = dml.DoubleMLPLR(dml_data, ml_g, ml_m)

In [29]: _ = dml_plr_obj.fit()

# define groups
In [30]: np.random.seed(42)

In [31]: groups = pd.DataFrame(np.random.choice(3, 500), columns=['Group'], dtype=str)

Group
0     2
1     0
2     2
3     2
4     0

In [33]: gate_obj = dml_plr_obj.gate(groups=groups)

In [34]: ci = gate_obj.confint()

In [35]: print(ci)
2.5 %    effect    97.5 %
Group_0  0.497964  0.644113  0.790261
Group_1  0.247617  0.395268  0.542919
Group_2  0.452114  0.593981  0.735848


A more detailed notebook on GATEs with DoubleMLPLR models is available in the example gallery.

## 4.2. Conditional average treatment effects (CATEs)#

The DoubleMLIRM and DoubleMLPLR classes contain the cate() method, which enables the estimation and construction of confidence intervals for CATEs after fitting the DoubleML object. To estimate CATEs, the user has to specify a pandas DataFrame containing the basis (e.g. B-splines) for the conditional treatment effects. This will construct and fit a DoubleMLBLP object. Confidence intervals can then be constructed via the confint() method. Jointly valid confidence intervals will be based on a gaussian multiplier bootstrap.

### 4.2.1. CATEs for IRM models#

Conditional Average Treatment Effects (CATEs) for DoubleMLIRM models consider the target parameters

$\theta_{0}(x) = \mathbb{E}[Y(1) - Y(0)| X=x]$

for a low-dimensional feature $$X$$, where $$Y(d)$$ the potential outcome with $$d \in \{0, 1\}$$.

Point estimates and confidence intervals can be obtained via the gate() and confint() methods.

In [36]: import numpy as np

In [37]: import pandas as pd

In [38]: import patsy

In [39]: import doubleml as dml

In [40]: from doubleml.datasets import make_irm_data

In [41]: from sklearn.ensemble import RandomForestRegressor

In [42]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [43]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [44]: np.random.seed(3333)

In [45]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [46]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [47]: dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)

In [48]: _ = dml_irm_obj.fit()

# define a basis with respect to the first variable
In [49]: design_matrix = patsy.dmatrix("bs(x, df=5, degree=2)", {"x":obj_dml_data.data["X1"]})

In [50]: spline_basis = pd.DataFrame(design_matrix)

0         1         2         3         4    5
0  1.0  0.000000  0.191397  0.782646  0.025958  0.0
1  1.0  0.342467  0.653991  0.000000  0.000000  0.0
2  1.0  0.460535  0.511022  0.000000  0.000000  0.0
3  1.0  0.000000  0.456552  0.543358  0.000091  0.0
4  1.0  0.046405  0.778852  0.174743  0.000000  0.0

In [52]: cate_obj = dml_irm_obj.cate(basis=spline_basis)

In [53]: ci = cate_obj.confint(basis=spline_basis)

2.5 %    effect    97.5 %
0  0.139921  0.644182  1.148443
1 -5.521085 -1.707197  2.106691
2 -9.190869 -2.944149  3.302571
3  0.022954  1.016429  2.009904
4 -0.568111  1.130526  2.829162


A more detailed notebook on CATEs for DoubleMLIRM models is available in the example gallery. The examples also include the construction of a two-dimensional basis with B-splines.

### 4.2.2. CATEs for PLR models#

Conditional Average Treatment Effects (CATEs) for DoubleMLPLR models consider a slightly adjusted version of the DoubleMLPLR model. Instead of considering a constant treatment effect $$\theta_0$$ for all observations, the adjusted model allows for a different effect based on groups.

\begin{align}\begin{aligned}Y = D \theta_0(X) + g_0(X) + \zeta, & &\mathbb{E}(\zeta | D,X) = 0,\\D = m_0(X) + V, & &\mathbb{E}(V | X) = 0,\end{aligned}\end{align}

where $$\theta_0(X)$$ denotes the heterogeneous treatment effect.

Point estimates and confidence intervals can be obtained via the gate() and confint() methods.

In [55]: import numpy as np

In [56]: import pandas as pd

In [57]: import patsy

In [58]: import doubleml as dml

In [59]: from doubleml.datasets import make_plr_CCDDHNR2018

In [60]: from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

In [61]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [62]: ml_m = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [63]: np.random.seed(3333)

In [64]: dml_data = make_plr_CCDDHNR2018(alpha=0.5, n_obs=500, dim_x=20)

In [65]: dml_plr_obj = dml.DoubleMLPLR(dml_data, ml_g, ml_m)

In [66]: _ = dml_plr_obj.fit()

# define a basis with respect to the first variable
In [67]: design_matrix = patsy.dmatrix("bs(x, df=5, degree=2)", {"x":obj_dml_data.data["X1"]})

In [68]: spline_basis = pd.DataFrame(design_matrix)

0         1         2         3         4    5
0  1.0  0.000000  0.191397  0.782646  0.025958  0.0
1  1.0  0.342467  0.653991  0.000000  0.000000  0.0
2  1.0  0.460535  0.511022  0.000000  0.000000  0.0
3  1.0  0.000000  0.456552  0.543358  0.000091  0.0
4  1.0  0.046405  0.778852  0.174743  0.000000  0.0

In [70]: cate_obj = dml_plr_obj.cate(basis=spline_basis)

In [71]: ci = cate_obj.confint(basis=spline_basis)

2.5 %    effect    97.5 %
0  0.393060  0.549645  0.706231
1  0.434519  0.600934  0.767349
2  0.382684  0.588000  0.793316
3  0.440364  0.572153  0.703942
4  0.421234  0.594241  0.767247


A more detailed notebook on CATEs for DoubleMLPLR models is available in the example gallery.

Theory: In the model above, it holds

$\begin{split}\mathbb{E}[Y|X] &= \mathbb{E}[\theta_0(X) D|X] + \mathbb{E}[g_0(X)|X] + \underbrace{\mathbb{E}[\varepsilon|X]}_{=\mathbb{E}[\mathbb{E}[\varepsilon|D, X]|X]=0}\\ &=\theta_0(X) \mathbb{E}[D|X] + g_0(X)\end{split}$

such that

$\underbrace{Y - \mathbb{E}[Y|X]}_{=:\tilde{Y}} = \theta_0(X) (\underbrace{D - \mathbb{E}[D|X]}_{=:\tilde{D}}) + \varepsilon.$

Remark that for the generated $$\sigma$$-agebras $$\sigma(\tilde{D})\subseteq \sigma(D,X)$$ implying

$\mathbb{E}[\epsilon|\tilde{D}] = \mathbb{E}[\mathbb{E}[\epsilon|X, D]|\tilde{D}] = 0$

and consequently

$\mathbb{E}[\tilde{Y}|\tilde{D}] = \theta_0(X)\tilde{D}.$

Consequently, $$\theta_0(X)$$ can be estimated by regressing $$\tilde{Y}$$ on $$\tilde{D}$$:

$\theta_0(X) = \arg\min_{\theta(X) \in \Theta}\mathbb{E}[(\tilde{Y} - \theta(X)\tilde{D})^2]$

The DoubleML implementation approximates the effect $$\theta_0(X)$$ by a linear projection on a supplied basis $$\phi(X)$$:

$\theta_0(X) \approx \beta_0^T \phi(X)$

where $$\beta_0$$ are coefficients to be estimated. The coverage of the confidence intervals is meant to include the the approximation $$\beta_0^T\phi(X)$$.

## 4.3. Weighted Average Treatment Effects#

The DoubleMLIRM class allows to specify weights via the weights argument in the initialization of the DoubleMLIRM object. Given some weights, $$\omega(Y,D,X)$$ the model identifies the weighted average treatment effect

$\theta_0 = \mathbb{E}[(g_0(1,X) - g_0(0,X))\omega(Y,D,X)].$

The interpretation depends on the choice of weights. The simplest examples include

• $$\omega(Y,D,X) = 1$$ which corresponds to the average treatment effect (ATE)

• $$\omega(Y,D,X) = \frac{1\{X\in G\}}{P(X\in G)}$$ which corresponds to the group average treatment effect (GATE) for group $$G$$

• $$\omega(Y,D,X) = \pi(X)$$ which corresponds to the average value of policy $$\pi$$, where $$0\le \pi \le 1$$

where the weights $$\omega(Y,D,X)$$ only depend on the features $$X$$.

In these cases the weights can be specified as an array via the weights argument in the initialization of the DoubleMLIRM object.

In [73]: import numpy as np

In [74]: import pandas as pd

In [75]: import doubleml as dml

In [76]: from doubleml.datasets import make_irm_data

In [77]: from sklearn.ensemble import RandomForestRegressor

In [78]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [79]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [80]: np.random.seed(3333)

In [81]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [82]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [83]: weights = np.ones(500)

In [84]: dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m, weights=weights)

In [85]: _ = dml_irm_obj.fit()

In [86]: print(dml_irm_obj.summary)
coef   std err         t     P>|t|     2.5 %    97.5 %
d  0.050856  0.664276  0.076559  0.938975 -1.251101  1.352813


If the weights do not only depend on the features $$X$$, but e.g. also on the treatment $$D$$ estimation becomes more involved. To identifiy the correct parameter not only the weights $$\omega(Y,D,X)$$ but also their conditional expectation

$\bar{\omega}(X) = \mathbb{E}[\omega(Y,D,X)|X]$

has to be specified. A common example is the average treatment effect on the treated (ATTE) which can be identified by setting

• $$\omega(Y,D,X) = \frac{D}{P(D=1)}$$

• $$\bar{\omega}(X) = \frac{\mathbb{E}[D|X]}{P(D=1)} = \frac{m_0(X)}{P(D=1)}$$

which depends on the propensity score $$m_0(X)$$. In this case the weights can be specified as a dictionary the weights argument in the initialization of the DoubleMLIRM object.

One other important example would be the sensitivity analysis for group average treatment effects on the treated (GATET). In this case the weights would take the following form

• $$\omega(Y,D,X) = \frac{1\{D=1, X\in G\}}{P(D=1, X\in G)}= \frac{D \cdot 1\{X\in G\}}{P(D=1, X\in G)}$$

• $$\bar{\omega}(X) = \frac{\mathbb{E}[D|X]1\{X\in G\}}{P(D=1, X\in G)} = \frac{m_0(X)1\{X\in G\}}{P(D=1, X\in G)}.$$

To simplify the specification of the weights, the DoubleMLIRM with score='ATTE' accepts binary weights, which should correspond to $$1\{X\in G\}$$. This automatically relies on the propensity score $$m(X)$$ to construct the weights mentioned above (e.g. for weights equal to one this refers to the average treatment effect on the treated).

A more detailed notebook on weighted average treatment effects for on GATE sensitivity analysis is available in the example gallery.

## 4.4. Quantiles#

The DoubleML package includes (local) quantile estimation for potential outcomes for IRM and IIVM models.

### 4.4.1. Potential quantiles (PQs)#

For a quantile $$\tau \in (0,1)$$ the target parameters $$\theta_{\tau}(d)$$ of interest are the potential quantiles (PQs),

$P(Y(d) \le \theta_{\tau}(d)) = \tau,$

and local potential quantiles (LPQs),

$P(Y(d) \le \theta_{\tau}(d)|\text{Compliers}) = \tau.$

where $$Y(d)$$ denotes the potential outcome with $$d \in \{0, 1\}$$.

DoubleMLPQ implements potential quantile estimation. Estimation is conducted via its fit() method:

In [87]: import numpy as np

In [88]: import doubleml as dml

In [89]: from doubleml.datasets import make_irm_data

In [90]: from sklearn.ensemble import RandomForestClassifier

In [91]: np.random.seed(3141)

In [92]: ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [93]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [94]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [95]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [96]: dml_pq_obj = dml.DoubleMLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)

In [97]: dml_pq_obj.fit().summary
Out[97]:
coef   std err         t     P>|t|     2.5 %    97.5 %
d  0.553878  0.149858  3.696011  0.000219  0.260161  0.847595


DoubleMLLPQ implements local potential quantile estimation, where the argument treatment indicates the potential outcome. Estimation is conducted via its fit() method:

In [98]: import numpy as np

In [99]: import doubleml as dml

In [100]: from doubleml.datasets import make_iivm_data

In [101]: from sklearn.ensemble import RandomForestClassifier

In [102]: np.random.seed(3141)

In [103]: ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [104]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [105]: data = make_iivm_data(theta=0.5, n_obs=1000, dim_x=20, return_type='DataFrame')

In [106]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd', z_cols='z')

In [107]: dml_lpq_obj = dml.DoubleMLLPQ(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)

In [108]: dml_lpq_obj.fit().summary
Out[108]:
coef   std err         t    P>|t|    2.5 %    97.5 %
d  0.217244  0.636453  0.341336  0.73285 -1.03018  1.464668


### 4.4.2. Quantile treatment effects (QTEs)#

For a quantile $$\tau \in (0,1)$$ the target parameter $$\theta_{\tau}$$ of interest are the quantile treatment effect (QTE),

$\theta_{\tau} = \theta_{\tau}(1) - \theta_{\tau}(0)$

where $$\theta_{\tau}(d)$$ denotes the corresponding potential quantile.

Analogously, the local quantile treatment effect (LQTE) can be defined as the difference of the corresponding local potential quantiles.

DoubleMLQTE implements quantile treatment effect estimation. Estimation is conducted via its fit() method:

In [109]: import numpy as np

In [110]: import doubleml as dml

In [111]: from doubleml.datasets import make_irm_data

In [112]: from sklearn.ensemble import RandomForestClassifier

In [113]: np.random.seed(3141)

In [114]: ml_g = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [115]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [116]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [117]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [118]: dml_qte_obj = dml.DoubleMLQTE(obj_dml_data, ml_g, ml_m, score='PQ', quantiles=[0.25, 0.5, 0.75])

In [119]: dml_qte_obj.fit().summary
Out[119]:
coef   std err         t     P>|t|     2.5 %    97.5 %
0.25  0.274825  0.347310  0.791297  0.428771 -0.405890  0.955541
0.50  0.449150  0.192539  2.332782  0.019660  0.071782  0.826519
0.75  0.709606  0.193308  3.670867  0.000242  0.330731  1.088482


To estimate local quantile effects the score argument has to be set to 'LPQ'. A detailed notebook on PQs and QTEs is available in the example gallery.

## 4.5. Conditional value at risk (CVaR)#

The DoubleML package includes conditional value at risk estimation for IRM models.

### 4.5.1. CVaR of potential outcomes#

For a quantile $$\tau \in (0,1)$$ the target parameters $$\theta_{\tau}(d)$$ of interest are the conditional values at risk (CVaRs) of the potential outcomes,

$\theta_{\tau}(d) = \frac{\mathbb{E}[Y(d) 1\{F_{Y(d)}(Y(d) \ge \tau)]}{1-\tau},$

where $$Y(d)$$ denotes the potential outcome with $$d \in \{0, 1\}$$ and $$F_{Y(d)}(x)$$ the corresponding cdf of $$Y(d)$$.

DoubleMLCVAR implements conditional value at risk estimation for potential outcomes, where the argument treatment indicates the potential outcome. Estimation is conducted via its fit() method:

In [120]: import numpy as np

In [121]: import doubleml as dml

In [122]: from doubleml.datasets import make_irm_data

In [123]: from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

In [124]: np.random.seed(3141)

In [125]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [126]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [127]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [128]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [129]: dml_cvar_obj = dml.DoubleMLCVAR(obj_dml_data, ml_g, ml_m, treatment=1, quantile=0.5)

In [130]: dml_cvar_obj.fit().summary
Out[130]:
coef   std err         t         P>|t|     2.5 %    97.5 %
d  1.588364  0.096616  16.43989  9.909942e-61  1.398999  1.777728


### 4.5.2. CVaR treatment effects#

For a quantile $$\tau \in (0,1)$$ the target parameter $$\theta_{\tau}$$ of interest are the treatment effects on the conditional value at risk,

$\theta_{\tau} = \theta_{\tau}(1) - \theta_{\tau}(0)$

where $$\theta_{\tau}(d)$$ denotes the corresponding conditional values at risk of the potential outcomes.

DoubleMLQTE implements CVaR treatment effect estimation, if the score argument has been set to 'CVaR' (default is 'PQ'). Estimation is conducted via its fit() method:

In [131]: import numpy as np

In [132]: import doubleml as dml

In [133]: from doubleml.datasets import make_irm_data

In [134]: from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor

In [135]: np.random.seed(3141)

In [136]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [137]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=10, min_samples_leaf=2)

In [138]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [139]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [140]: dml_cvar_obj = dml.DoubleMLQTE(obj_dml_data, ml_g, ml_m, score='CVaR', quantiles=[0.25, 0.5, 0.75])

In [141]: dml_cvar_obj.fit().summary
Out[141]:
coef   std err         t         P>|t|     2.5 %    97.5 %
0.25  0.474731  0.246624  1.924921  5.423921e-02 -0.008642  0.958105
0.50  0.691911  0.143495  4.821855  1.422293e-06  0.410667  0.973156
0.75  1.001714  0.166375  6.020819  1.735369e-09  0.675625  1.327803


A detailed notebook on CVaR estimation for potential outcomes and treatment effects is available in the example gallery.

## 4.6. Policy Learning with Trees#

Policy Learning considers to find an optimal decision policy. We consider deterministic binary policies, which are defined as mapping

$\pi: X\mapsto \{0,1\}.$

Using the score component $$\psi_b(W_i,\hat{\eta})$$ of the IRM score, we can find the optimal treatment policy by solving the weighted classification problem

$\hat{\pi} = \mathop{\arg \max}\limits_{\pi\in\Pi} \frac{1}{n}\sum_{i=1}^n(2\pi(X_i)-1)\hat{\psi_b(W_i,\hat{\eta})},$

where $$\Pi$$ denotes a policy class, which we define as depth-$$m$$ classification trees. Thus, we estimate splits in the features $$X$$ that reflect the heterogeneity of the treatment effect and consequently maximize the sum of the estimated individual treatment effects of all individuals by assigning different treatments.

The DoubleMLIRM class contains the policy_tree() method, which enables the estimation of a policy tree using weighted classification after fitting the DoubleMLIRM object. To estimate a policy tree, the user has to specify a pandas DataFrame containing the covariates on based on which the policy will make treatment decisions. These can be either the original covariates used in the DoubleMLIRM estimation, or a subset, or new covariates. This will construct and fit a DoubleMLPolicyTree object. A plot of the decision rules can be displayed by the plot_tree() method. The predict() method enables the application of the estimated policy on new data. The depth parameter, which defaults to 2, can be used to adjust the maximum depth of the tree.

In [142]: import numpy as np

In [143]: import pandas as pd

In [144]: import doubleml as dml

In [145]: from doubleml.datasets import make_irm_data

In [146]: from sklearn.ensemble import RandomForestRegressor, RandomForestClassifier

In [147]: ml_g = RandomForestRegressor(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [148]: ml_m = RandomForestClassifier(n_estimators=100, max_features=20, max_depth=5, min_samples_leaf=2)

In [149]: np.random.seed(3333)

In [150]: data = make_irm_data(theta=0.5, n_obs=500, dim_x=20, return_type='DataFrame')

In [151]: obj_dml_data = dml.DoubleMLData(data, 'y', 'd')

In [152]: dml_irm_obj = dml.DoubleMLIRM(obj_dml_data, ml_g, ml_m)

In [153]: _ = dml_irm_obj.fit()

# define features to learn policy on
In [154]: np.random.seed(42)

In [155]: features = data[["X1","X2","X3"]]

X1        X2        X3
0  0.305133  0.497298 -0.811398
1 -0.696770 -1.717860 -2.030087
2 -0.903135  0.174940  0.185585
3  0.095475 -0.653820 -1.800272
4 -0.198953  0.203893  0.204653

# fits a tree of depth 2
In [157]: policy_tree_obj = dml_irm_obj.policy_tree(features=features)

In [158]: policy_tree_obj.plot_tree();


A more detailed notebook on Policy Trees is available in the example gallery.