Causal Machine Learning and the Resource Curse with Python EconML

Overview

Can natural resource wealth be both a blessing and a curse? And can local institutions determine which way it goes? In this tutorial, we use EconML’s CausalForestDML to estimate heterogeneous causal effects of mining and mineral prices on economic development — and test whether institutional quality moderates those effects differently for mining versus price shocks.

We use simulated data with known ground-truth parameters so we can verify that the method recovers the correct answers. The simulated dataset mirrors the structure of Hodler, Lechner & Raschky (2023), who studied 3,800 Sub-Saharan African districts using a Modified Causal Forest. This tutorial focuses on the DML methodology: how the Double Machine Learning framework separates nuisance estimation from causal effect estimation to produce valid, efficient heterogeneous treatment effect estimates.

For the economic narrative and a companion implementation in Stata 19, see Causal Machine Learning and the Resource Curse with Stata 19.

Learning objectives

By the end of this tutorial, you will be able to:

1. Understand the Double Machine Learning (DML) framework and why residualization enables valid causal inference
2. Distinguish heterogeneity features (X) from nuisance controls (W) in CausalForestDML
3. Configure CausalForestDML for discrete multi-valued treatments with panel data
4. Estimate Average Treatment Effects (ATEs) and Group Average Treatment Effects (GATEs) with proper BLB inference
5. Interpret GATE patterns to identify which variables moderate treatment effects
6. Use EconML-specific tools like SingleTreeCateInterpreter for data-driven subgroup discovery
7. Evaluate results against known ground-truth parameters

The DML Causal Forest

What is a Conditional Average Treatment Effect?

The Conditional Average Treatment Effect (CATE) measures how a treatment effect varies across individuals with different characteristics:

$$\tau(\mathbf{x}) = E\{Y_i(1) - Y_i(0) \mid \mathbf{X}_i = \mathbf{x}\}$$

In words: $\tau(\mathbf{x})$ is the expected difference in potential outcomes for a unit with covariates $\mathbf{x}$. When $\tau(\mathbf{x})$ varies across $\mathbf{x}$, we have treatment effect heterogeneity — the treatment helps some units more than others.

The Partial Linear Model

EconML’s CausalForestDML estimates CATEs within the partially linear model:

$$Y = T \cdot \tau(\mathbf{x}) + g_0(\mathbf{x}, \mathbf{w}) + \varepsilon, \qquad T = m_0(\mathbf{x}, \mathbf{w}) + v$$

where $g_0(\cdot)$ and $m_0(\cdot)$ are flexible nuisance functions estimated by machine learning (Gradient Boosting in our case), $\tau(\mathbf{x})$ is the heterogeneous treatment effect function we want to learn, $\mathbf{x}$ are the covariates that may moderate the treatment effect, and $\mathbf{w}$ are additional controls used only in the first-stage nuisance models.

The key insight of Double Machine Learning (Chernozhukov et al., 2018) is to residualize both the outcome and the treatment before fitting the causal forest. Think of residualization like noise-canceling headphones: the first stage removes the “background noise” of confounders from both the outcome and treatment, so the causal forest only hears the “signal” of the treatment effect. This two-step approach has a property called Neyman orthogonality: first-stage estimation errors have only a second-order impact on the causal estimates. This means the causal forest remains valid even when the nuisance models converge at slower-than-parametric rates.

Three levels of effects

The causal forest produces per-observation CATE estimates, which aggregate to three levels:

Level	Name	What it measures
CATE	Conditional ATE	Effect for an individual with covariates $\mathbf{x}$
GATE	Group ATE	Average effect for a subgroup defined by a variable $Z$
ATE	Average TE	Overall average across all units

DML pipeline

flowchart LR
    A["<b>Panel Data</b><br/>3,000 obs"]:::data
    B["<b>First Stage</b><br/>GBM nuisance<br/>models"]:::first
    C["<b>Residualize</b><br/>Y&#771; = Y - g&#770;(X,W)<br/>T&#771; = T - m&#770;(X,W)"]:::resid
    D["<b>Causal Forest</b><br/>500 honest trees"]:::forest
    E["<b>CATEs</b><br/>Per-observation<br/>effects"]:::cate

    A --> B --> C --> D --> E

    classDef data fill:#6a9bcc,stroke:#141413,color:#fff
    classDef first fill:#d97757,stroke:#141413,color:#fff
    classDef resid fill:#00d4c8,stroke:#141413,color:#141413
    classDef forest fill:#141413,stroke:#d97757,color:#fff
    classDef cate fill:#6a9bcc,stroke:#141413,color:#fff

Setup and configuration

We use CausalForestDML from EconML with Gradient Boosting nuisance models. The ground-truth parameters are defined inline so the tutorial is fully self-contained.

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
from econml.dml import CausalForestDML
from sklearn.ensemble import (GradientBoostingRegressor,
                              GradientBoostingClassifier)

# Ground-truth ATEs from the data-generating process
TRUE_ATES = {
    '1-0': 0.250,  # Mining effect
    '2-0': 0.300,  # Mining + medium price
    '3-0': 0.550,  # Mining + high price
    '2-1': 0.050,  # Medium price premium (small)
    '3-1': 0.300,  # High price premium (large)
    '3-2': 0.250,  # High vs medium step
}

Load the simulated data

The dataset simulates 300 districts across 8 countries observed over 10 years (2003–2012), following the structure of Hodler, Lechner & Raschky (2023). Treatment has four levels: no mining (0), mining at low prices (1), medium prices (2), and high prices (3).

DATA_URL = ("https://github.com/cmg777/starter-academic-v501"
            "/raw/master/content/post/python_EconML/sim_resource_curse.csv")
df = pd.read_csv(DATA_URL)
print(f"Dataset: {len(df):,} observations")
print(f"Districts: {df['district_id'].nunique()}, "
      f"Countries: {df['country_id'].nunique()}")

Dataset: 3,000 observations
Districts: 300, Countries: 8

The dataset contains 3,000 district-year observations with a heavily imbalanced treatment: 85% of observations are untreated (no mining), while each of the three mining groups comprises only 5% of the data. This imbalance makes causal inference challenging — the causal forest must learn from relatively few treated observations.

Descriptive statistics

Treatment distribution

labels = {0: 'No mining', 1: 'Low prices',
          2: 'Med prices', 3: 'High prices'}
for t, n in df['treatment'].value_counts().sort_index().items():
    print(f"  {t} ({labels[t]}): {n:,} ({n/len(df):.1%})")

  0 (No mining): 2,550 (85.0%)
  1 (Low prices): 150 (5.0%)
  2 (Med prices): 150 (5.0%)
  3 (High prices): 150 (5.0%)

Treatment distribution across the four groups. The 85/5/5/5 imbalance makes causal inference challenging.

The 85/5/5/5 split means the causal forest has 2,550 control observations but only 150 per treatment level. For within-mining comparisons (e.g., 3-1), only 300 observations contribute, making standard errors larger for price-effect estimates.

Outcomes by treatment group

for t in sorted(df['treatment'].unique()):
    mask = df['treatment'] == t
    m_ntl = df.loc[mask, 'ntl_log'].mean()
    m_conf = df.loc[mask, 'conflict'].mean()
    print(f"  {t} ({labels[t]}):  NTL={m_ntl:.3f}  Conflict={m_conf:.1%}")

  0 (No mining):   NTL=-1.137  Conflict=10.7%
  1 (Low prices):  NTL=-1.028  Conflict=18.0%
  2 (Med prices):  NTL=-0.930  Conflict=18.0%
  3 (High prices): NTL=-0.615  Conflict=28.0%

The raw means show a clear gradient: higher treatment levels are associated with higher NTL and higher conflict rates. But these raw comparisons are biased because mining districts differ systematically from non-mining districts in geography, institutions, and economic development.

Naive comparison: why we need causal ML

for comp in ['1-0', '2-1', '3-1']:
    a, b = int(comp[0]), int(comp[2])
    naive = df.loc[df['treatment']==a, 'ntl_log'].mean() - \
            df.loc[df['treatment']==b, 'ntl_log'].mean()
    truth = TRUE_ATES[comp]
    print(f"  {comp}: Naive={naive:.3f}  Truth={truth:.3f}  Bias={naive-truth:+.3f}")

  1-0: Naive=0.109  Truth=0.250  Bias=-0.141
  2-1: Naive=0.098  Truth=0.050  Bias=+0.048
  3-1: Naive=0.413  Truth=0.300  Bias=+0.113

The naive 1-0 estimate of 0.109 is severely biased downward from the true effect of 0.250 — a 56% underestimate. This happens because mining districts tend to have worse geographic and institutional characteristics that independently reduce development. The DML Causal Forest removes this selection bias by residualizing both the outcome and the treatment against observed confounders before estimating the causal effect.

EconML estimation

Configuration

We separate covariates into two groups with distinct roles in the DML framework:

• X features (10 variables): Enter the causal forest and can drive treatment effect heterogeneity. These include exec_constraints, quality_of_govt, gdp_pc, elevation, temperature, ruggedness, distance_capital, agri_suitability, population, and ethnic_frac.
• W controls (2 variables): Used only in the first-stage nuisance models (country_id, year). These absorb country and time fixed effects but do not enter the causal forest.

X_COLS = ['exec_constraints', 'quality_of_govt', 'gdp_pc',
          'elevation', 'temperature', 'ruggedness',
          'distance_capital', 'agri_suitability', 'population',
          'ethnic_frac']
W_COLS = ['country_id', 'year']

Fitting the model

Y = df['ntl_log'].values
T = df['treatment'].values
X = df[X_COLS].values
W = df[W_COLS].values

est_ntl = CausalForestDML(
    model_y=GradientBoostingRegressor(n_estimators=200, max_depth=4,
                                      random_state=42),
    model_t=GradientBoostingClassifier(n_estimators=200, max_depth=4,
                                       random_state=42),
    discrete_treatment=True,
    categories=[0, 1, 2, 3],
    n_estimators=500,
    min_samples_leaf=10,
    honest=True,       # Separate split/estimation samples
    inference=True,    # BLB confidence intervals
    cv=5,              # 5-fold cross-fitting
    n_jobs=1,
    random_state=42,
)
est_ntl.fit(Y, T, X=X, W=W, groups=df['district_id'].values)

  NTL: fitted in 25s

Several configuration choices deserve explanation. Honest trees use separate subsamples for choosing splits versus estimating leaf values — like having one team write the exam questions and a different team take the exam, this prevents the tree from “memorizing” the training data and enables valid confidence intervals. GroupKFold via groups=district_id ensures that cross-fitting (splitting data into K folds, training nuisance models on K-1 folds, and predicting on the held-out fold) does not split observations from the same district across folds, preventing data leakage in panel data. Note that this does not provide clustered standard errors — it only prevents within-district information from leaking across folds.

Causal identification

The Causal Forest requires the Conditional Independence Assumption (CIA): treatment assignment is independent of potential outcomes conditional on observed covariates $(X, W)$. In our simulated data, the CIA holds by construction because all confounders are observed. In real data, unobserved confounders (geological surveys, political connections) could bias the estimates.

Average Treatment Effects

EconML’s ate_inference() provides ATEs with proper confidence intervals via the Bootstrap of Little Bags (BLB) method — a computationally efficient bootstrap that resamples within subsets (“bags”) of the data to estimate uncertainty without refitting the entire forest. We compute all six pairwise treatment contrasts:

comparisons = [
    ('1-0', 0, 1), ('2-0', 0, 2), ('3-0', 0, 3),
    ('2-1', 1, 2), ('3-1', 1, 3), ('3-2', 2, 3),
]

for comp_label, t0, t1 in comparisons:
    res = est_ntl.ate_inference(X, T0=t0, T1=t1)
    lo, hi = res.conf_int_mean(alpha=0.1)
    print(f"  {comp_label}: ATE={res.mean_point:.4f} "
          f"SE={res.stderr_mean:.4f} 90%CI=[{lo:.3f}, {hi:.3f}]")

  1-0: ATE=0.2398  SE=0.0702  90%CI=[0.124, 0.355]
  2-0: ATE=0.2684  SE=0.0791  90%CI=[0.138, 0.399]
  3-0: ATE=0.4598  SE=0.0811  90%CI=[0.327, 0.593]
  2-1: ATE=0.0286  SE=0.1008  90%CI=[-0.137, 0.194]
  3-1: ATE=0.2200  SE=0.1014  90%CI=[0.053, 0.387]
  3-2: ATE=0.1914  SE=0.1092  90%CI=[0.012, 0.371]

Finding 1: Mining increases economic development and conflict. All three mining-vs-no-mining contrasts (1-0, 2-0, 3-0) are positive and highly significant. The ATE for the basic mining effect (1-0) is 0.240, close to the ground truth of 0.250. This represents a 24% increase in nighttime lights from mining activity, after controlling for geographic and institutional confounders.

Finding 2: Price effects are non-linear. The contrast 2-1 (medium vs low prices) is 0.029 and statistically insignificant ($p > 0.10$) — medium prices add essentially nothing beyond the basic mining effect. But the contrast 3-1 (high vs low prices) is 0.220 and significant at the 5% level. This asymmetry confirms that price effects “jump” only at high commodity prices. The DML Causal Forest correctly recovers this non-linearity from the data.

Treatment effect heterogeneity (GATEs)

Computing GATEs manually

Unlike Stata 19’s cate command which computes GATEs automatically, EconML requires manual computation. We estimate individual-level CATEs via effect_inference(), group observations by an institutional variable, and compute the mean CATE within each group:

def compute_gate(est, df, z_var, t0, t1):
    inf = est.effect_inference(X, T0=t0, T1=t1)
    ite, ite_se = inf.point_estimate, inf.stderr

    for z in sorted(df[z_var].unique()):
        mask = df[z_var].values == z
        gate = ite[mask].mean()
        # Propagate BLB standard errors
        gate_se = np.sqrt(np.mean(ite_se[mask]**2) / mask.sum())

The standard error formula sqrt(mean(se_i^2) / n) propagates the per-observation BLB standard errors to the group level, capturing estimation uncertainty rather than just within-group heterogeneity.

GATEs by Executive Constraints

The mining effect (1-0) should vary with institutional quality, while the price effect (3-1) should be flat:

GATEs for the mining effect (1-0) by executive constraints. The upward slope shows that stronger institutions amplify the economic benefits of mining.

GATEs for the price effect (3-1) by executive constraints. The flat pattern confirms that institutions do not moderate price effects.

  1-0 (Mining vs No Mining):
  Exec. Constr.       GATE               90% CI      N
  ----------------------------------------------------
              1      0.175   [0.168, 0.182]    300
              2      0.255   [0.249, 0.262]    330
              3      0.240   [0.236, 0.244]    720
              4      0.242   [0.238, 0.246]    780
              5      0.243   [0.237, 0.250]    420
              6      0.264   [0.259, 0.269]    450
  Range: 0.089

  3-1 (High vs Low Prices):
  Exec. Constr.       GATE               90% CI      N
  ----------------------------------------------------
              1      0.242   [0.232, 0.252]    300
              2      0.197   [0.187, 0.206]    330
              3      0.217   [0.211, 0.224]    720
              4      0.227   [0.221, 0.233]    780
              5      0.224   [0.216, 0.231]    420
              6      0.211   [0.204, 0.219]    450
  Range: 0.045

Finding 3: Institutions moderate mining effects but NOT price effects. The mining effect GATEs (1-0) show a range of 0.089 across executive constraint levels, with the lowest effect (0.175) at the weakest institutions (exec_constraints=1) rising to 0.264 at the strongest (exec_constraints=6). The price effect GATEs (3-1) show a much narrower range of only 0.045, with no clear monotone pattern. This asymmetric pattern — institutions shaping the mining-vs-no-mining margin but not the price margin — is exactly the structural finding embedded in the DGP and reported in Hodler, Lechner & Raschky (2023).

GATEs by Quality of Government

The same pattern appears when we use a continuous institutional measure:

GATEs for the mining effect (1-0) by quality of government. The positive relationship cross-validates the executive constraints finding.

GATEs for the price effect (3-1) by quality of government. The flat pattern is consistent across institutional measures.

The mining effect (1-0) shows a positive relationship with quality of government, while the price effect (3-1) remains approximately flat across the institutional quality distribution. This cross-validates Finding 3 using a different institutional measure.

Variable importance

EconML computes feature importances as the normalized contribution of each variable to treatment effect heterogeneity across all forest splits:

importances = est_ntl.feature_importances_

  distance_capital           0.172
  ethnic_frac                0.141
  ruggedness                 0.135
  population                 0.126
  agri_suitability           0.120
  temperature                0.120
  elevation                  0.120
  gdp_pc                     0.036
  quality_of_govt            0.019
  exec_constraints           0.010

Feature importance for treatment effect heterogeneity. Geographic variables dominate splitting frequency, but institutional variables are the true moderators.

Geographic variables dominate the importances because they have continuous variation that the forest can split on finely. Institutional variables (exec_constraints, quality_of_govt) rank lower despite being the true moderators in the DGP — they have limited discrete values (6 levels for executive constraints), so the forest cannot split on them as frequently. This illustrates an important caveat: feature importance measures splitting frequency, not causal importance for moderation. The GATE analysis (which directly tests moderation) is more informative than feature importance for answering the question “which variables moderate treatment effects?”

CATE Interpreter

EconML provides a SingleTreeCateInterpreter that fits a shallow decision tree to the estimated CATEs, creating interpretable subgroups. This is an EconML-specific feature not available in Stata’s cate command.

from econml.cate_interpreter import SingleTreeCateInterpreter

intrp = SingleTreeCateInterpreter(max_depth=2, min_samples_leaf=100)
intrp.interpret(est_ntl, X)
intrp.plot(feature_names=X_COLS)

Decision tree summarizing CATE heterogeneity for the mining effect (1-0). The shallow tree identifies data-driven subgroups with different treatment effects.

The interpreter tree identifies data-driven subgroups with different treatment effects using a depth-2 tree. This provides a complementary view to the GATE analysis: while GATEs test pre-specified hypotheses about institutional moderation, the CATE interpreter discovers subgroups that the analyst may not have considered.

Discussion

Limitations

• No clustered standard errors: EconML does not support clustered SEs natively. We use GroupKFold by district to prevent data leakage, but this does not account for within-district correlation in the standard errors. The companion Stata tutorial uses Stata 19’s cate command which handles clustering directly.
• Contemporaneous outcomes: The full paper uses treatment at time $t$ and outcome at $t+1$, strengthening causal identification. Our simulated data uses contemporaneous treatment and outcomes.
• Simplified covariates: The real analysis uses 60+ covariates; we use 12. The simulated DGP guarantees that the CIA holds — all confounders are observed by construction.

Assumptions

The CATE estimates rely on the Conditional Independence Assumption: treatment is independent of potential outcomes given $(X, W)$. In observational settings, this assumption is untestable and may be violated by unobserved confounders. With simulated data, we know the assumption holds.

Summary and next steps

1. EconML’s CausalForestDML recovered all three ground-truth findings. The ATE for the basic mining effect (1-0 = 0.240) closely matches the true value of 0.250. Price effects are correctly identified as non-linear (2-1 = 0.029 n.s., 3-1 = 0.220 significant). GATE patterns confirm that institutions moderate mining effects (range = 0.089) but not price effects (range = 0.045).

2. The DML framework is the key methodological contribution. By residualizing both the outcome and treatment in a first stage, DML achieves Neyman orthogonality — making the causal forest robust to errors in the nuisance models. This is particularly valuable when the outcome and treatment processes are complex.

3. Feature importance can be misleading for moderation analysis. Geographic variables dominate the forest’s splitting importances, but institutional variables are the true moderators. GATE analysis is more appropriate for testing specific moderation hypotheses.

4. The CATE interpreter provides data-driven subgroup discovery. Unlike pre-specified GATEs, the shallow decision tree finds the variables and thresholds that best separate high-effect from low-effect subgroups.

For the economic story behind these findings and a parallel implementation using Stata 19’s built-in cate command, see the companion tutorial: Causal Machine Learning and the Resource Curse with Stata 19.

Exercises

1. Replace the nuisance models. Swap GradientBoostingRegressor with RandomForestRegressor(n_estimators=200). Do the ATE and GATE estimates change? Why or why not (think about Neyman orthogonality)?

2. Vary the number of trees. Try n_estimators=100 vs n_estimators=1000. How do the standard errors and GATE patterns change?

3. Test the GroupKFold assumption. Remove groups=df['district_id'].values from the fit() call. What happens to the confidence intervals?

4. Discretize quality of government. Create quartiles of quality_of_govt and compute GATEs on the quartiles instead of raw values. Do the patterns become clearer?

5. Explore the CATE interpreter depth. Increase max_depth from 2 to 4 in SingleTreeCateInterpreter. Do the additional splits reveal meaningful subgroups or just noise?

References

1. Hodler, R., Lechner, M., & Raschky, P.A. (2023). Institutions and the resource curse: New insights from causal machine learning. PLoS ONE, 18(6), e0284968.
2. Chernozhukov, V., Chetverikov, D., Demirer, M., Duflo, E., Hansen, C., Newey, W., & Robins, J. (2018). Double/Debiased Machine Learning for Treatment and Structural Parameters. The Econometrics Journal, 21(1), C1–C68.
3. Athey, S., Tibshirani, J., & Wager, S. (2019). Generalized Random Forests. The Annals of Statistics, 47(2), 1148–1178.
4. Sachs, J.D. & Warner, A.M. (1995). Natural Resource Abundance and Economic Growth. NBER Working Paper No. 5398.
5. Mehlum, H., Moene, K., & Torvik, R. (2006). Institutions and the Resource Curse. The Economic Journal, 116(508), 1–20.
6. EconML Documentation — PyWhy