CausalGAN: Learning Causal Implicit Generative Models with Adversarial Training

By Murat Kocaoglu, Christopher Snyder, A. Dimakis, Sriram Vishwanath. 2017

314 citations recorded by the source corpus.

We propose an adversarial training procedure for learning a causal implicit generative model for a given causal graph. We show that adversarial training can be used to learn a generative model with true observational and interventional distributions if the generator architecture is consistent with the given causal graph. We consider the application of generating faces based on given binary labels where the dependency structure between the labels is preserved with a causal graph. This problem can be seen as learning a causal implicit generative model for the image and labels. We devise a two-stage procedure for this problem. First we train a causal implicit generative model over binary labels using a neural network consistent with a causal graph as the generator. We empirically show that WassersteinGAN can be used to output discrete labels. Later, we propose two new conditional GAN architectures, which we call CausalGAN and CausalBEGAN. We show that the optimal generator of the CausalGAN, given the labels, samples from the image distributions conditioned on these labels. The conditional GAN combined with a trained causal implicit generative model for the labels is then a causal implicit generative model over the labels and the generated image. We show that the proposed architectures can be used to sample from observational and interventional image distributions, even for interventions which do not naturally occur in the dataset.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Genetics, Generative Models

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Citations in the corpus

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA (2021, background): “Of works which learn CGMs, a number assume the causal graph structure is known (Kocaoglu et al., 2018; Shen et al., 2021; Nair et al., 2019).”
A theory of independent mechanisms for extrapolation in generative models (2020, background): “Deep neural network have been leveraged in causal inference for learning causal graphs between observed variables (Lopez-Paz and Oquab 2016) and associated causal effects (Louizos et al. 2017; Shalit, Johansson, and Sontag 2017; Kocaoglu et al. 2017; Lachapelle et al. 2019; Zhu, Ng, and Chen 2019).”
Conditional variance penalties and domain shift robustness (2017, background): “the problems of causeeffect inference and generative adversarial networks (Chalupka et al., 2014; Lopez-Paz et al., 2017; Lopez-Paz and Oquab, 2017; Goudet et al., 2017; Bahadori et al., 2017; Besserve et al., 2018; Kocaoglu et al., 2018).”
Explaining Classifiers with Causal Concept Effect (CaCE) (2019, methodology): “This can range from simply pasting a cropped bicycle image, to using sophisticated generative models such as CausalGAN [10] to add such objects.”
COPHY: Counterfactual Learning of Physical Dynamics (2019, background): “In the ML community, causal reasoning gained mainstream attention relatively recently (Lopez-Paz et al., 2017; Lopez-Paz & Oquab, 2017; Kocaoglu et al., 2018; Rojas-Carulla et al., 2018; Mooij et al., 2016; Sch¨olkopf et al., 2012), due to limitations of statistical learning becoming increasingly…”
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications (2020, background): “There are many papers related to GANs [126]–[131] such as CSGAN [132] and LOGAN [133].”
Geometrical Insights for Implicit Generative Modeling (2017, background): “• The structure of the generator function Gθ(z) could be directly interpreted as a set of equations describing a physical or causal model of the data [28].”
A Metaverse: Taxonomy, Components, Applications, and Open Challenges (2022, unknown): “Research on graph models and graph convolution networks is increasing to extend the links of stories [110]–[113].”
Explaining Visual Models by Causal Attribution (2019, methodology): “[11]) uses a GAN architecture coupled with a Causal Controller Graph.”
Multiple Causal Inference with Latent Confounding (2018, background): “Tran and Blei [ 37 ] build implicit models for genome-wide association studies and describe general implicit causal models in the same vein as Kocaoglu et al. [ 19 ] .”
Guided Image Generation with Conditional Invertible Neural Networks (2019, background): “CausalGANs [25] train two additional discriminator networks, called “labeler” and “anti-labeler”, with the latter explicitly penalizing the lack of diversity.”
Causal Induction from Visual Observations for Goal Directed Tasks (2019, unknown): “Causal modeling has been explored in several contexts, including image classiﬁcation (Chalupka et al., 2014), generative models (Kocaoglu et al., 2017), robot planning (Kurutach et al., 2018), policy learning (Buesing et al., 2018), and transfer learning (Bengio et al., 2019).”
Deep Generative Design: Integration of Topology Optimization and Generative Models (2019, methodology): “The learning rate parameter is set with reference to the settings of previous papers that studied image quality using BEGAN [48,49].”
Using Wasserstein Generative Adversarial Networks for the design of Monte Carlo simulations (2019, unknown): “[2018], Kocaoglu et al. [2017]).”
Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017, methodology): “ causeeect inference and generative adversarial networks (Chalupka et al., 2014; Lopez-Paz et al., 2017; Lopez-Paz and Oquab, 2017; Goudet et al., 2017; Bahadori et al., 2017; Besserve et al., 2018; Kocaoglu et al., 2018). Kilbertus et al. (2017) exploit causal reasoning to characterize fairness con”