From d4b7a615cc07674328861c9dfc913cdbc2db3ad9 Mon Sep 17 00:00:00 2001 From: AliFozooni Date: Sat, 15 Aug 2026 11:02:47 -0700 Subject: [PATCH] Correct R-learner nonparametric interpretation --- ...Debiased-Orthogonal-Machine-Learning.ipynb | 134 ++++++++++-------- 1 file changed, 75 insertions(+), 59 deletions(-) diff --git a/causal-inference-for-the-brave-and-true/22-Debiased-Orthogonal-Machine-Learning.ipynb b/causal-inference-for-the-brave-and-true/22-Debiased-Orthogonal-Machine-Learning.ipynb index 6f77d4cd..7ddbc48d 100644 --- a/causal-inference-for-the-brave-and-true/22-Debiased-Orthogonal-Machine-Learning.ipynb +++ b/causal-inference-for-the-brave-and-true/22-Debiased-Orthogonal-Machine-Learning.ipynb @@ -892,26 +892,28 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Not a huge improvement here, but it's something. Plus, not having to specify the functional form of the treatment function is already a huge benefit. \n", + "Not a huge improvement here, but it's something. Plus, not having to specify the functional form of how the CATE varies with the covariates is already a huge benefit. \n", "\n", "\n", "### What is Non-Parametric About?\n", " \n", - "Before we move on, I just wanted to highlight a common misconception. When we think about using a non-parametric Double-ML model to estimate the CATE, it looks like we will get a nonlinear treatment effect. For instance, let's assume a very simple data generating process (DGP) where discont affects sales non-linearly, but through a square root function. \n", + "Before we move on, I want to highlight a common misconception. In the R-learner above, *non-parametric* refers to the final model for $\\tau(X)$: we can learn an arbitrary function of the pre-treatment covariates $X$. It does not make the outcome an arbitrary function of the treatment. At a fixed value of $X$, the model still imposes a linear relationship between the residualized outcome and residualized treatment, $\\tilde{Y}=\\tau(X)\\tilde{T}+e$. In other words, it can learn that different units have different treatment slopes, but it does not automatically learn that the slope for the same unit changes with the treatment level.\n", + " \n", + "To see the distinction, consider a simple data generating process (DGP) with a nonlinear dose-response: \n", " \n", "$\n", - "Sales_i = 20 + 10*\\sqrt{Discount_i} + e_i\n", + "Sales_i(t) = 20 + 10\\sqrt{t} + e_i\n", "$\n", " \n", - "The treatment effect is given by the derivative of this Sales function with respect to the treatment.\n", + "The marginal effect at treatment level $t$ is the derivative of this sales function with respect to the treatment.\n", " \n", "$\n", - "\\dfrac{\\partial Sales_i}{\\partial Discount_i} = \\dfrac{10}{2\\sqrt{Discount_i}}\n", + "\\dfrac{\\partial Sales_i(t)}{\\partial t} = \\dfrac{5}{\\sqrt{t}}\n", "$\n", " \n", - "As we can see, the treatment effect is **not** linear. It actually gets weaker as the treatment increases. This makes a lot of sense for this DGP. At first, a little bit of discount increases sales by a lot. But, as we give too much discount, an extra unit of discount will affect sales less and less, because people won't want to buy to infinity. Hence, the discount is only effective up until they point they get satiated. \n", + "The dose-response is nonlinear in the treatment, even though its formula is linear in the numeric coefficient 10. Its marginal effect gets weaker as the treatment increases. At first, a little more discount increases sales by a lot. As the discount grows, an extra unit of discount affects sales less and less. \n", " \n", - "The question then is, can the non-parametric ML capture this saturating behavior in the treatment effect? Can it extrapolate from a small discount level that, if the discount were higher, the treatment effect would be lower? The answer is... sort of. To better understand that, let's generate data like in the above DGP." + "Can the non-parametric R-learner above recover this saturating dose-response? Not from this DGP. There are no pre-treatment covariates $X$, so $\\tau(X)$ can only be a constant: the best linear slope over the observed treatment distribution. To make this limitation concrete, let's generate data from the DGP above." ] }, { @@ -972,7 +974,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now, let's apply the Non-Parametric Double/Debias ML to this data. " + "Now let's apply the R-learner correctly. Because this DGP has no pre-treatment features, the nuisance functions reduce to unconditional means and $\\tau(X)$ reduces to one constant slope. Feeding `discount` or `discount_res` to a final CATE model would use the treatment itself as though it were a covariate $X$, changing the estimand rather than estimating $\\tau(X)$." ] }, { @@ -984,42 +986,49 @@ "start_time": "2023-07-27T16:57:52.244607Z" } }, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "1.0496297150376859" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "debias_m = LGBMRegressor(max_depth=3)\n", - "denoise_m = LGBMRegressor(max_depth=3)\n", - "\n", - "# orthogonalising step\n", - "discount_res = discount.ravel() - cross_val_predict(debias_m, np.ones(discount.shape), discount.ravel(), cv=5)\n", - "sales_res = sales.ravel() - cross_val_predict(denoise_m, np.ones(sales.shape), sales.ravel(), cv=5)\n", - "\n", - "# final, non parametric causal model\n", - "non_param = LGBMRegressor(max_depth=3)\n", - "w = discount_res ** 2 \n", - "y_star = sales_res / discount_res\n", + "# with no pre-treatment X, residualizing means centering\n", + "discount_res = discount.ravel() - discount.mean()\n", + "sales_res = sales.ravel() - sales.mean()\n", "\n", - "non_param.fit(X=discount_res.reshape(-1,1), y=y_star.ravel(), sample_weight=w.ravel());" + "# the R-loss has one constant minimizer: the residual-on-residual slope\n", + "tau_hat = np.sum(discount_res * sales_res) / np.sum(discount_res ** 2)\n", + "tau_hat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "With the above model, we can get the CATE estimate. The issue here is that the CATE is not linear. As the treatment increases, the CATE should decrease. The question we are trying to answer is if the non-parametric model can capture that non linearity. \n", + "With no covariates, the R-loss reduces to\n", " \n", - "To answer that properly, let's remember what is the underlying assumption that the Double/Debiased ML makes about the data generating process. These assumptions can be seen in the equation we've laid down before.\n", + "$$\n", + "\\hat{L}_n(\\tau) = \\frac{1}{n}\\sum_{i=1}^n(\\tilde{Y}_i-\\tau\\tilde{T}_i)^2,\n", + "$$\n", " \n", - "$\n", - "\\tilde{Y}_i = \\tau(X_i) \\tilde{T}_i + e_i\n", - "$\n", + "whose minimizer is\n", " \n", - "In words, it says that the residualized outcome is equal to the residualized treatment multiplied by the conditional treatment effect. This mean that the **treatment impacts the outcome linearly**. There is no non-linearity here. The above model says that the outcome will increase by a fixed amount $\\tau(X_i) $ if we increase the treatment from 1 to 10 or from 100 to 110. It's a simple multiplication. \n", + "$$\n", + "\\hat{\\tau}=\\frac{\\sum_i \\tilde{T}_i\\tilde{Y}_i}{\\sum_i \\tilde{T}_i^2}.\n", + "$$\n", " \n", - "So, does this mean that the non-parametric model can't capture the non-linearity of the treatment effect? Again, not really... Rather, what is happening is that Double/ML **finds the locally linear approximation to the non-linear CATE**. In other words, it finds the derivative of the outcome with respect to the treatment at that treatment level or around the treatment. This is equivalent to finding the slopes of the lines that are tangential to the outcome function at the treatment point.\n", + "This is the slope of the best global linear projection of the residualized outcome on the residualized treatment. The transformed target $\\tilde{Y}_i/\\tilde{T}_i$ is an algebraic device: weighting each ratio by $\\tilde{T}_i^2$ recovers this regression slope. An individual ratio is not a derivative, and the fitted slope is not a tangent to the outcome curve at that observation's treatment level.\n", " \n", - "![img](./data/img/debiased-ml/linear-aprox.png)\n", + "Fitting a flexible model of that pseudo-outcome on `discount_res` would not fix this limitation. Here each pseudo-outcome is a noisy secant slope measured from the center $(\\bar{T}, \\bar{Y})$, and `discount_res` is the centered treatment, not a pre-treatment feature. Moreover, fitting on `discount_res` and then predicting on the raw `discount` scale mixes two different coordinate systems. Neither operation identifies the local derivative of the dose-response.\n", " \n", - "This mean that, yes, Non-Parametric Double-ML will figure out that the treatment effect will be smaller as we increase the treatment. But, no, it won't find the non-linear treatment effect, but rather the local linear treatment effect. We can even plot those linear approximations against the ground true non-linear causal effect and indeed, they are good approximations. " + "When genuine pre-treatment covariates are present, a flexible final learner can estimate a different conditional slope $\\tau(x)$ in different regions of $X$. Here there are no such features, so the model returns one slope. The plots below compare its global linear approximation with the nonlinear dose-response and compare that one fitted slope with the true marginal-effect curve." ] }, { @@ -1037,33 +1046,34 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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", "text/plain": [ - "
" + "
" ] }, - "metadata": { - "needs_background": "light" - }, + "metadata": {}, "output_type": "display_data" } ], "source": [ - "cate = non_param.predict(X=discount)\n", + "sales_linear = sales.mean() + tau_hat * (discount - discount.mean())\n", "\n", "plt.figure(figsize=(15,5))\n", "plt.subplot(1,2,1)\n", - "plt.scatter(discount, sales)\n", - "plt.plot(discount, 20 + 10*np.sqrt(discount), label=\"Ground Truth\", c=\"C1\")\n", - "plt.title(\"Sales by Discount\")\n", + "plt.scatter(discount, sales, alpha=0.15, s=8, label=\"Observed sales\")\n", + "plt.plot(discount, 20 + 10*np.sqrt(discount), label=\"True dose-response\", c=\"C1\")\n", + "plt.plot(discount, sales_linear, label=\"Best linear projection\", c=\"C4\")\n", + "plt.title(\"Nonlinear Dose-Response\")\n", "plt.xlabel(\"Discount\")\n", + "plt.ylabel(\"Sales\")\n", "plt.legend()\n", "\n", "plt.subplot(1,2,2)\n", - "plt.scatter(discount, cate, label=\"$\\hat{\\\\tau}(x)$\", c=\"C4\")\n", - "plt.plot(discount, 5/np.sqrt(discount), label=\"Ground Truth\", c=\"C2\")\n", - "plt.title(\"CATE ($\\partial$Sales) by Discount\")\n", + "plt.plot(discount, 5/np.sqrt(discount), label=\"True marginal effect\", c=\"C2\")\n", + "plt.axhline(tau_hat, label=f\"R-learner slope = {tau_hat:.2f}\", c=\"C4\")\n", + "plt.title(\"Marginal Effect vs. R-Learner Slope\")\n", "plt.xlabel(\"Discount\")\n", + "plt.ylabel(\"Sales per Unit of Discount\")\n", "plt.legend();" ] }, @@ -1071,21 +1081,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "This might sound like technicalities, but it has very practical consequences. For example, let's say you find a treatment effect of 2 for a customer in the example above, meaning that if you increase the discount by 1 unit, your sales to that customer will increase by 2 units. You might look at that and think: \"Great! I'll give a lot of discounts to this unit! After all, for every 1 unit in discount, I'll get 2 in sales\". However, that's the wrong conclusion. The treatment effect is 2 only at that discount level. As soon as you increase the discount, the effect will fall. For example, say this hypothetical customer got only 5 in discount, which is why her treatment effect is so high. Say you see that huge treatment effect and use it to justify giving 20 in discount to that customer. But as you do so, the effect might go from 2 to something like 0.5. And a 20 discount that made sense at a treatment effect of 2 might no longer be profitable at a treatment effect of 0.5.\n", + "This distinction has practical consequences. An R-learner estimate $\\tau(x)$ can tell us that customers with different pre-treatment features have different treatment slopes. For a fixed $x$, however, the model uses that same slope throughout the treatment range. If the true dose-response is nonlinear, multiplying $\\hat{\\tau}(x)$ by a large treatment change can badly misstate the outcome change. In the example above, the one fitted slope is an average linear approximation over the observed discount distribution, not an effect tied to each customer's observed discount.\n", " \n", - "This means you have to be extra careful when extrapolating a nonlinear treatment effect to a new treatment level. If you are not, you might end up making very unprofitable decisions. Another way to put is is that, when treatment effect is not linear, even non-parametric Double/Debiased-ML will **struggle to make counterfactuals outcome predictions**. It will try to linearly extrapolate the treatment effect (TE) from a low treatment level to a high treatment level or the other way around. And, due to the non linearity, that extrapolation will likely be off. \n", + "If the goal is to learn how effects vary with the level of a continuous treatment, we need a method designed for a continuous dose-response or marginal-effect curve, together with the corresponding identification and overlap assumptions. Making the CATE learner non-parametric in $X$ does not by itself provide that curve or counterfactual outcome predictions at new treatment levels. \n", " \n", - "To solve that, there is a final idea. Keep in mind that this idea is much less scientific than the things we've seen before. It boils down to using a S-learner after applying the orthogonalization procedure, but I'm getting ahead of myself. Let's look at that next. \n", + "The final idea below instead explores a heuristic: use an S-learner-like model after residualization to predict over treatment levels. This changes the objective and does not inherit the R-learner guarantees above, so treat it as an illustration rather than a solution to continuous-treatment effect estimation. \n", "\n", "![img](./data/img/debiased-ml/non-sci.png)\n", "\n", "## Non-Scientific Double/Debiased ML\n", "\n", - "The final idea we will try is a fundamental shift in mentality. We will no longer try to estimate the linear approximation to the CATE. Instead, we will make counterfactual predictions.\n", + "This final idea shifts the target from the R-learner's conditional slope $\\tau(X)$ to predictions over candidate treatment levels.\n", " \n", "![img](./data/img/debiased-ml/cf-pred.png)\n", " \n", - "The CATE is the slope of the outcome function at the data point. It is how much we expect the outcome to change if we increase the treatment by a very small amount. More technically, it's the derivative at the point. Counterfactual predictions, on the other hand, are an attempt to recreate the entire outcome curve from a single datapoint. We will predict what the outcome would be if the treatment were at some other level than the one it currently takes, hence the counterfactual. \n", + "For a nonlinear continuous-treatment response, the derivative at a particular treatment level is a marginal effect. A counterfactual outcome curve is a broader object: it gives the outcome we would expect for a unit under each candidate treatment level. The partially linear R-learner above does not estimate either object as a function of the treatment level. \n", " \n", "If we manage to do so, we will be able to simulate different treatments for a unit and predict how it would respond under those different treatment levels. This is very risky business, because we will be extrapolating an entire curve from a single point. Also, although I've used this technique in practice a lot, I've never found any scientific article showing how or why it works. That's why I call it the Non-Scientific Double-ML. Simply put: beware!\n", " \n", @@ -1095,15 +1105,15 @@ "\\tilde{Y}_i = \\tau(X_i) \\tilde{T}_i + e_i\n", "$\n", " \n", - "Now, I'll move the treatment inside the treatment effect function. This allows the treatment effect to be non linear, that is to change with the treatment itself. \n", + "The heuristic replaces the linear treatment term with an unrestricted prediction function $f$: \n", " \n", "$\n", - "\\tilde{Y}_i = \\tau(X_i, \\tilde{T}_i) + e_i\n", + "\\tilde{Y}_i = f(X_i, \\tilde{T}_i) + e_i.\n", "$\n", " \n", - "This is dangerous business, because I have no idea how this treatment functions. For all we know, it could be some weird non-linear function. But, fortunately, we know how to estimate weird functions with Machine Learning. So, that's what we will do. Simply speaking, we will fit a ML model to predict the residualised outcome $\\tilde{Y}$ from the residualized treatment $\\tilde{T}$ together with the features $X$. The residualisation is important to remove bias and noise so that this final ML can focus on learning only the treatment effect and how the covariates $X$ impact that treatment effect. \n", + "We will fit an ML model to predict the residualized outcome $\\tilde{Y}$ from the residualized treatment $\\tilde{T}$ together with the features $X$. Notice that $f$ is a residual-outcome prediction function, not the CATE $\\tau(X)$ from the R-loss. Residualization still removes variation explained by the observed $X$, but changing the final-stage objective means the orthogonality argument developed above no longer establishes that $f$ is a causal dose-response. \n", " \n", - "Then, once we have this model, we will make 2 step counterfactual predictions. First we will have to make a prediction for the treatment in order to get $\\tilde{T}$, then, we will feed that prediction, along with the features, in our final model $\\hat{\\tau}(X_i, \\tilde{T}_i)$.\n", + "To evaluate this model at a candidate treatment $t$, we first use the treatment model to form the residualized candidate $\\tilde{T}_i(t)=t-\\hat{M}_t(X_i)$. We then feed that residual together with the features into the final model $\\hat{f}(X_i, \\tilde{T}_i(t))$.\n", " \n", "Since we will have to make $\\tilde{T}$, we first need to implement our own version of the `cross_prediction` function. This function will return not only the cross prediction, but also the models used to make those predictions." ] @@ -1195,15 +1205,15 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now is where things get a little weird. If we think about it, this final ML model is estimating the following $\\tau$ treatment function\n", + "This final ML model estimates the residual-outcome response function introduced above:\n", " \n", "$\n", - "\\tilde{Y}_i = \\tau(X_i, \\tilde{T}_i) + e_i\n", + "\\tilde{Y}_i = f(X_i, \\tilde{T}_i) + e_i.\n", "$\n", " \n", - "but there isn't a clear way to extract the treatment effect from this function. So, rather than extracting a treatment effect, we will input the counterfactual predictions, just like I've shown in the previous image. We will simulate different price levels for each unit and use our Double-ML model to predict what would be the sales we would see under those different price levels. \n", + "The function $f$ is not itself a treatment effect. Rather than extracting a CATE from it, the heuristic evaluates its residualized-outcome predictions over a grid of candidate prices. These predictions should not be confused with an identified dose-response merely because the inputs were residualized. \n", "\n", - "To achieve that, we will 1) cross join the test set with a price table that contains all simulated prices. The end result will be as follows" + "To create that grid, we cross join the test set with a table containing all the candidate prices. The result is as follows." ] }, { @@ -1374,15 +1384,21 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Notice that we are showing only the day in index 1, so only a single unit. On that day (unit), the actual or factual price or treatment was 7. But we've simulated different counterfactual treatments, from 3 to 10. Now, we will feed all those counterfactual prices to our causal model, which will make counterfactual sales predictions based on those simulated prices.\n", + "Notice that we are showing only the day at index 0, so only a single unit. On that day, the factual price was 7, but we have generated candidate prices from 3 to 10. We will feed those candidate prices to the heuristic prediction model.\n", " \n", "Since our model has the following format\n", " \n", "$\n", - "\\widehat{Price_i} = \\hat{\\tau}(X_i, \\tilde{T}_i)\n", + "\\widehat{\\tilde{Y}}_i(t) = \\hat{f}(X_i, \\tilde{T}_i(t)),\n", + "$\n", + " \n", + "we first need the residualized candidate price\n", + " \n", + "$\n", + "\\tilde{T}_i(t) = t - \\hat{M}_t(X_i).\n", "$\n", " \n", - "Before making the counterfactual predictions, we need to get $\\tilde{T}_i$, that is, the price residuals. We will get those residuals by first, making predictions will all our treatment models (remember that we've used a 5 fold cross prediction in the training step), then we will average the predictions from the five models into a single prediction and finally subtract the counterfactual price we've generated earlier from the predicted price using this ensemble of models." + "For each test unit, we make predictions with all five treatment models fitted during cross-validation and average them to obtain $\\hat{M}_t(X_i)$. We then subtract that predicted price from each candidate price $t$—the same direction used for the training residuals. Because $\\hat{M}_t(X_i)$ depends on the unit's features but not on the candidate $t$, it can be computed once per unit and reused across the treatment grid. The code below evaluates it on the expanded table, which repeats the same value for each copy of a unit but is algebraically equivalent." ] }, { @@ -1563,7 +1579,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, we now have a sales prediction for every simulated price. The lower the price, the higher the sales. One interesting thing is that these predictions are off in their level. For instance, they go from about 24 to about -24. That's because the model is predicting the residualized outcome, which is roughly mean zero. This is fine if all you want is to get the slope of the sales curve, which is the price treatment effect. Also, if you want to fix the prediction levels, all you have to do is add the predictions from the denoising model $M_y$. " + "As you can see, we now have a residualized-outcome prediction for every simulated price. The lower the price, the higher the prediction. These predictions are off in their level: they go from about 24 to about -24 because the model predicts an outcome residual, which is roughly mean zero. To recover an outcome-level prediction, we can add the denoising prediction from $M_y$. Even then, the slope of this heuristic prediction curve is not automatically an identified price treatment effect; that interpretation requires a valid dose-response model and assumptions that the residualization argument above does not establish." ] }, {