diff --git a/Python/bayesian_cuped.ipynb b/Python/bayesian_cuped.ipynb index c24a93d..e17e256 100644 --- a/Python/bayesian_cuped.ipynb +++ b/Python/bayesian_cuped.ipynb @@ -1,5 +1,16 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bayesian CUPED & Sensitivity Analysis\n", + "\n", + "Motivated by the great blog post by [Understanding CUPED](https://towardsdatascience.com/understanding-cuped-a822523641af) by [Matteo Courthoud](https://medium.com/@matteo.courthoud), we explore a Bayesian approach to [CUPED](https://dl.acm.org/doi/abs/10.1145/2433396.2433413) to understand its sensitivity with respect to the covariance parameter $\\theta$ of the pre-post mean model. I will assume that the reader is already familiar with CUPED and has read Matteo's blog post (highly recommended!). Here we focus on the sensitivity component. We do not do this in full generality but rather focus on the specific example of Matteo's blog post.\n", + "\n", + "**Warning:** Please do not take this results too seriously. This is just a sensitivity analysis I simply felt like doing.\n" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -20,6 +31,7 @@ "import numpyro\n", "import numpyro.distributions as dist\n", "import pandas as pd\n", + "import seaborn as sns\n", "from jax import random\n", "from numpyro.handlers import do\n", "from numpyro.infer import MCMC, NUTS\n", @@ -43,33 +55,17 @@ ] }, { - "cell_type": "code", - "execution_count": 2, + "cell_type": "markdown", "metadata": {}, - "outputs": [], "source": [ - "def generate_data(rng, alpha=5, beta=0, gamma=3, delta=2, n=100, seed=1):\n", - " np.random.seed(seed)\n", - "\n", - " # Individuals\n", - " individual = range(1, n + 1)\n", - "\n", - " # Treatment status\n", - " d = rng.binomial(1, 0.5, n)\n", - "\n", - " # Individual outcome pre-treatment\n", - " y0 = alpha + beta * d + rng.normal(0, 1, n)\n", - " y1 = y0 + gamma + delta * d + rng.normal(0, 1, n)\n", + "## Generate Data\n", "\n", - " # Generate the dataframe\n", - " return pd.DataFrame(\n", - " {\"individual\": individual, \"ad_campaign\": d, \"revenue0\": y0, \"revenue1\": y1}\n", - " )" + "We use a synthetic dataset from Matteo's repository [`src.dgp`](https://github.com/matteocourthoud/Blog-Posts/blob/main/notebooks/src/dgp.py). We simulate a pre and post experiment `revenue` variable under the presence of an intervention `ad_campaign` in a randomized controlled trial." ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [ { @@ -148,16 +144,81 @@ "4 5 0 4.643736 7.734321" ] }, - "execution_count": 3, + "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data_df = generate_data(rng)\n", + "def generate_data(rng, alpha=5, beta=0, gamma=3, delta=2, n=100, seed=1):\n", + " np.random.seed(seed)\n", + "\n", + " # Individuals\n", + " individual = range(1, n + 1)\n", + "\n", + " # Treatment status\n", + " d = rng.binomial(1, 0.5, n)\n", + "\n", + " # Individual outcome pre-treatment\n", + " y0 = alpha + beta * d + rng.normal(0, 1, n)\n", + " y1 = y0 + gamma + delta * d + rng.normal(0, 1, n)\n", + "\n", + " # Generate the dataframe\n", + " return pd.DataFrame(\n", + " {\"individual\": individual, \"ad_campaign\": d, \"revenue0\": y0, \"revenue1\": y1}\n", + " )\n", + "\n", + "\n", + "data_df: pd.DataFrame = generate_data(rng)\n", + "\n", "data_df.head()" ] }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "image/png": { + "height": 611, + "width": 1011 + } + }, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(nrows=2, ncols=1, sharex=True, sharey=True, layout=\"constrained\")\n", + "\n", + "sns.kdeplot(data=data_df, x=\"revenue0\", hue=\"ad_campaign\", fill=True, ax=ax[0])\n", + "ax[0].set_title(\"Revenue Pre-Campaign\")\n", + "\n", + "sns.kdeplot(data=data_df, x=\"revenue1\", hue=\"ad_campaign\", fill=True, ax=ax[1])\n", + "ax[1].set_title(\"Revenue Post-Campaign\");" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Effect Estimation: Difference in Means" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can compute the point estimate of the difference in means as follows:" + ] + }, { "cell_type": "code", "execution_count": 4, @@ -180,6 +241,13 @@ "print(f\"Difference in means: {difference_in_means:.3f}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We are of course interested in quantifying the uncertainty of this estimate. We can do this using a simple linear regression model. We use a Bayesian approach with non-very informative priors, to be able to compare the results with the CUPED approach." + ] + }, { "cell_type": "code", "execution_count": 5, @@ -268,7 +336,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -305,7 +373,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "aace612e1d2c40bfbaf204d85b08ed7f", + "model_id": "45bd695c00014f6fb43ce69cdec54d6b", "version_major": 2, "version_minor": 0 }, @@ -319,7 +387,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "38e7c38b0e7a42e59fd80d87863fa1dc", + "model_id": "0e7c2b99797f4627a3d4bbf3ce790466", "version_major": 2, "version_minor": 0 }, @@ -333,7 +401,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d3c26c64c6674725b2cdbd9834bcc438", + "model_id": "cdb49493678c4764a3adc06886ecb99e", "version_major": 2, "version_minor": 0 }, @@ -347,7 +415,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "d50531ed18f5468a8f51e340e8afc462", + "model_id": "e2e9bd446d4e470490424f8dd0793427", "version_major": 2, "version_minor": 0 }, @@ -394,6 +462,15 @@ "ax.set_title(r\"Posterior distribution of $\\beta$\");" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the posterior distribution of the difference in means is centered around the true value of $2$ (from the data generating process).\n", + "\n", + "Let's compute the standard deviation of the posterior distribution:" + ] + }, { "cell_type": "code", "execution_count": 8, @@ -411,6 +488,24 @@ "print(f\"std: {idata_difference_in_means['posterior']['beta'].std().item():.3f}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Bayesian CUPED\n", + "\n", + "We now turn to the Bayesian CUPED approach. Recall the algorithm for the CUPED estimator:\n", + "\n", + "1. Regress $\\text{revenue}_1$ on $\\text{revenue}_0$ and estimate the $\\theta$ coefficient.\n", + "2. Compute ${revenue}_{cuped} = \\bar{\\text{revenue}}_1 - \\theta \\times \\bar{\\text{revenue}}_0$.\n", + "3. Compute the difference of $\\widehat{\\text{revenue}}_{cuped}$ between treatment and control group.\n", + "\n", + "\n", + "**The motivation of this simulation is:** How does the uncertainty on $\\theta$ impact the uncertainty of the CUPED estimate?\n", + "\n", + "To answer this question, we implement these steps in a single Bayesian model (with same non-informative priors as before)." + ] + }, { "cell_type": "code", "execution_count": 9, @@ -552,7 +647,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 9, @@ -605,7 +700,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c9ad5fef72174cf2834f034cdf22a4ae", + "model_id": "25a032ca5eec405eada4cdbdff1977f4", "version_major": 2, "version_minor": 0 }, @@ -619,7 +714,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "2086b48a08ef495bbc4a91df6add914e", + "model_id": "ce20439b6afd49efb7864e23d2faffe8", "version_major": 2, "version_minor": 0 }, @@ -633,7 +728,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "6dff4a1ea6514ba280b3529634be88c2", + "model_id": "42d300de644e44fdb6da1b37f6c7bdf4", "version_major": 2, "version_minor": 0 }, @@ -647,7 +742,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "dee835c5ecdd4904974d6a46064a9507", + "model_id": "caa7590dda174199be4f017694403580", "version_major": 2, "version_minor": 0 }, @@ -670,6 +765,13 @@ "mcmc_cuped.run(rng_subkey, revenue1, ad_campaign, revenue0)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "First, let's look into the posterior distribution of $\\theta$:" + ] + }, { "cell_type": "code", "execution_count": 11, @@ -698,6 +800,15 @@ "ax.set_title(r\"Posterior distribution of $\\theta$\");" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The point estimate of $\\theta$ is centered around the true value of $1.1$. \n", + "\n", + "Next we look into the posterior distribution of the CUPED effect estimate:" + ] + }, { "cell_type": "code", "execution_count": 12, @@ -744,6 +855,20 @@ "print(f\"std: {idata_cuped['posterior']['beta_cuped'].std().item():.3f}\")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As expected, the posterior distribution of the CUPED effect estimate is narrower than the one of the simple difference in means. So far so good! The result's match Matteo's blog post." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "For the sake of completeness, let's compare the posterior distributions of the difference in means and the CUPED effect estimate:" + ] + }, { "cell_type": "code", "execution_count": 14, @@ -793,6 +918,24 @@ "fig.suptitle(\"Model Comparison\", fontsize=18, fontweight=\"bold\");" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here we have a clear visual evidence of the variance reduction of the CUPED model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Graph Surgery\n", + "\n", + "We now turn to the sensitivity analysis. We want to see how the uncertainty of the CUPED estimate evolves with respect to the uncertainty of the $\\theta$ coefficient.\n", + "\n", + "To do this, we use the [`do`](https://num.pyro.ai/en/stable/handlers.html#do) operator to set the value of $\\theta$ to the mean of its posterior distribution. This way we can mimic what we would have done in a traditional two-step approach.\n" + ] + }, { "cell_type": "code", "execution_count": 15, @@ -922,7 +1065,7 @@ "\n" ], "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -943,6 +1086,20 @@ ")" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Notice that we have removed the relationship between $\\theta$ and the `revenue1 ~ revenue0` model.\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Next, we fit the later model of the CUPED algorithm:" + ] + }, { "cell_type": "code", "execution_count": 16, @@ -951,7 +1108,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "c819a0c577ab4ba2b993b56e474b9f29", + "model_id": "e9435d6d4b2a48af8710bcac477f4d75", "version_major": 2, "version_minor": 0 }, @@ -965,7 +1122,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "126655b536e9474bad08a88062eb3b12", + "model_id": "04b5d5209bcc4f2d9be140538965583f", "version_major": 2, "version_minor": 0 }, @@ -979,7 +1136,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "41beb5ee0fcb4a19867ebd3e33a5778d", + "model_id": "1e0f9e8fe0744365af2c276ede5b293c", "version_major": 2, "version_minor": 0 }, @@ -993,7 +1150,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "9b15e9b968294047b9b7ce5f14efa53f", + "model_id": "97f1db456e51474bb2016a84c6e5857c", "version_major": 2, "version_minor": 0 }, @@ -1018,6 +1175,15 @@ "idata_do_cuped = az.from_numpyro(mcmc_do_cuped)" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Comparison\n", + "\n", + "Finally, we compare all the effect estimation of the three models. In particular, we focus on the standard deviation of the posterior distributions." + ] + }, { "cell_type": "code", "execution_count": 17, @@ -1075,6 +1241,15 @@ "\n", "fig.suptitle(\"Model Comparison\", fontsize=18, fontweight=\"bold\");" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We see that the estimate and uncertainty of the full Bayesian CUPED model is very similar (almost identical) to the one of the CUPED model after graph surgery. The latter standard deviation is just slightly smaller (approximately $1\\%$) than the one of the full Bayesian CUPED model.\n", + "\n", + "I wonder if this effect could be made more pronounced with a weaker relationship between $\\text{revenue}_1$ and $\\text{revenue}_0$. If you have any input on this, please let me know!" + ] } ], "metadata": {