Adaptive A/B Testing with Customer Features

We use a logistic interaction model: \(\eta(x,z) = x^\top\beta + z\,x^\top\gamma\), \(y \sim \mathrm{Bernoulli}(\sigma(\eta(x,z)))\). Here \(x\) denotes the feature vector \(\phi(x_1,x_2)=[1,\,x_1,\,x_2,\,x_1x_2]\). The heterogeneous A/B difference is \(\Delta(x)=\eta(x,1)-\eta(x,0)=x^\top\gamma\).

In this demo, the expected information gain view focuses on learning the interaction weights \(\gamma\) (i.e., the feature-dependent A/B difference), not the baseline \(\beta\). Any other random quantity computable from the model could be targeted depending on the goal (e.g., a policy boundary, a threshold, or a ranking), each leading to different computed optimal designs. The decision uncertainty view focuses on uncertainty in the sign of \(\Delta(x)\) ("is A better than B here?") rather than uncertainty in the absolute conversion rate.

Simulation setup for these precomputed frames: customer features \((x_1, x_2)\in[-3,7]^2\). We start with \(N_0=10\) customers sampled from the same proposal distribution used below. Each step draws a candidate pool of 200 customers from this proposal distribution, where each coordinate is sampled independently as \(\mathcal{N}(\mu=3,\sigma=1)\) with rejection outside \([-3,7]\); after 10,000 rejected draws the implementation falls back to clamping a fresh normal draw into \([-3,7]\). The OED policy chooses \(x^*\) from this candidate pool to maximize expected information gain. Ad assignment at \(x^*\) is randomized with \(P(z=1)=0.5\); outcomes are simulated from fixed “true” parameters.