Women in Data Science Puget Sound 2024

This interdisciplinary talk introduces the listeners to the power of Generative AI in the field of Causal Inference and its subsequent applications in Economics and Political Science. Our rigorous year-long research aims to develop a state-of-the-art Causal Inference technique: CausalGANs. Generative Adversarial Networks (GANs) is a popular deep learning method which dominates the field of image generation. We harness the essence of GANs to create, from scratch, a causal inference technique which modifies the architecture of GANs to solve the fundamental problem of missing counterfactuals in Causal Inference. In this thorough research, we set up a new framework, develop the notation, write mathematical proofs, and produce robust results by running over 200 parallelised experiments for each different set of parameters on High Power Computing. The GANs algorithm simultaneously trains two models: a generator and a discriminator. The generator's objective is to find a data-generating process that generates fake data emulating the distribution of real data and the discriminator's objective is to distinguish the real data from the fake data. This adversarial nature makes this framework a minimax game between its two components; the competition in this game drives both generator and discriminator to improve their methods until the simulated samples are indistinguishable from the observed samples. At the core of the GANs algorithm is the search for a neural network model that can generate fake data, whose distribution is independent of the labeling of real versus fake data. Independence restrictions of this kind are front and center in causal inference models, where the distribution of potential outcomes under treatment and control, conditional on contextual variables, are independent of the realized treatment. This makes the GANs apparatus a good method for causal inference, where instead of pitting real versus fake data, we now strive to get distributions of potential outcomes for treated and non-treated as close as possible. The ongoing research involves the development of the method, proof of its validity, and conducting empirical experiments. We confirm several intuitions as we test different aspects of the method, CausalGANs, with a robust evaluation strategy and compare it against traditional and other state-of-the-art methods in causal inference. We were able to empirically verify the mathematical theorems defined for the framework: 1) We can recover the parameters of the data-generating process through this adversarial framework, 2) The minimum of the loss function is attained close to the true data parameters, and 3) The minimizer provides the best estimator of the propensity score. Through this framework, we successfully obtain the treatment effects. Thus, the success of this method revolutionizes the field of economics through practical applications such as policy development, which often seeks to find the causal effect of interventions.