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Title: A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms

Paper content:
We propose to use a meta-learning objective that maximizes the speed of transfer on a modified distribution to learn how to modularize acquired knowledge.
In particular, we focus on how to factor a joint distribution into appropriate conditionals, consistent with the causal directions.
We explain when this can work, using the assumption that the changes in distributions are localized (e.g. to one of the marginals, for example due to an intervention on one of the variables).
We prove that under this assumption of localized changes in causal mechanisms, the correct causal graph will tend to have only a few of its parameters with non-zero gradient, i.e. that need to be adapted (those of the modified variables).
We argue and observe experimentally that this leads to faster adaptation, and use this property to define a meta-learning surrogate score which, in addition to a continuous parametrization of graphs, would favour correct causal graphs.
Finally, motivated by the AI agent point of view (e.g. of a robot discovering its environment autonomously), we consider how the same objective can discover the causal variables themselves, as a transformation of observed low-level variables with no causal meaning.
Experiments in the two-variable case validate the proposed ideas and theoretical results.
The data used to train our models is often assumed to be independent and identically distributed (iid.), according to some unknown distribution.
Likewise, the performance of a model is typically evaluated using test samples from the same distribution, assumed to be representative of the learned system's usage.
While these assumptions are well analyzed from a statistical point of view, they are rarely satisfied in many real-world applications.
For example, a medical diagnosis system trained on historical data from one hospital might perform poorly on patients from another institution, due to shifts in distribution.
Ideally, we would like our models to generalize well and adapt quickly to out-of-distribution data.
However, this comes at a price -in order to successfully transfer to a novel distribution, one might need additional information about data at hand.
In this paper, we are not considering assumptions on the data distribution but rather on how it changes (e.g., when going from a training distribution to a transfer distribution, possibly resulting from some agent's actions).
We focus on the assumption that the changes are sparse when the knowledge is represented in an appropriately modularized way, with only one or a few of the modules having changed.
This is especially relevant when the distributional change is due to actions by one or more agents, because agents intervene at a particular place and time, and this is reflected in the form of the interventions discussed in the causality literature (Pearl, 2009; Peters et al., 2016) , where a single causal variable is clamped to a particular value or a random variable.
In general, it is difficult for agents to influence many underlying causal variables at a time, and although this paper is not about agent learning as such, this is a property of the world that we propose to exploit here, to help discovering these variables and how they are causally related to each other.
In this context, the causal graph is a powerful tool because it tells us how perturbations in the distribution of intervened variables will propagate to all other variables and affect their distributions.
As expected, it is often the case that the causal structure is not known in advance.
The problem of causal discovery then entails obtaining the causal graph, a feat which is in general achievable only with strong assumptions.
One such assumption is that a learner that has learned to capture the correct structure of the true underlying data-generating process should still generalize to the case where the structure has been perturbed in a certain, restrictive way.
This can be illustrated by considering the example of temperature and altitude (Peters et al., 2017) : a learner that has learned to capture the mechanisms of atmospheric physics by learning that it makes more sense to predict temperature from the altitude (rather than vice versa) given training data from (say) Switzerland will still remain valid when tested on out-of-distribution data from a less mountainous country like (say) the Netherlands.
It has therefore been suggested that the out-of-distribution robustness of predictive models be used to guide the inference of the true causal structure (Peters et al., 2016; 2017) .
How can we exploit the assumption of localized change?
As we explain theoretically and verify experimentally here, if we have the right knowledge representation, then we should get fast adaptation to the transfer distribution when starting from a model that is well trained on the training distribution.
This arises because of our assumption that the ground truth data generative process is obtained as the composition of independent mechanisms, and that very few ground truth mechanisms and parameters need to change when going from the training distribution to the transfer distribution.
A model capturing a corresponding factorization of knowledge would thus require just a few updates, a few examples, for this adaptation to the transfer distribution.
As shown below, the expected gradient on the unchanged parameters would be near 0 (if the model was already well trained on the training distribution), so the effective search space during adaptation to the transfer distribution would be greatly reduced, which tends to produce fast adaptation, as found experimentally.
Thus, based on the assumption of small change in the right knowledge representation space, we can define a meta-learning objective that measures the speed of adaptation, i.e., a form of regret, in order to optimize the way in which knowledge should be represented, factorized and structured.
This is the core idea presented in this paper.
Returning to the example of temperature and altitude: when presented with out-of-distribution data from the Netherlands, we expect the correct model to adapt faster given a few transfer samples of actual weather data collected in the Netherlands.
Analogous to the case of robustness, the adaptation speed can then be used to guide the inference of the true causal structure of the problem at hand, possibly along with other sources of signal about causal structure.
Contributions.
We first verify on synthetic data that the model that correctly captures the underlying causal structure adapts faster when presented with data sampled after a performing certain interventions on the true two-variable causal graph (which is unknown to the learner).
This suggests that the adaptation speed can indeed function as score to assess how well the learner fits the underlying causal graph.
We then use a smooth parameterization of the considered causal graph to directly optimize this score in an end-to-end manner.
Finally, we show in a simple setting that the score can be exploited to disentangle the correct causal variables given an unknown mixture of the said variables.
Although this paper focuses on the causal graph, the proposed objective is motivated by the more general question of discovering the underlying causal variables and their dependencies to explain the environment of the learner, and make it possible for that learner to plan appropriately under changes due to interventions, either from self or from another agent.
The discovery of underlying explanatory variables has come under different names, in particular the notion of disentangling underlying variables (Bengio et al., 2013; Locatello et al., 2019) , and studied in the causal setting (Chalupka et al., 2015; 2017) and domain adaptation (Magliacane et al., 2018) .
This paper is also related to meta-learning (Finn et al., 2017; Finn, 2018; Alet et al., 2018; Dasgupta et al., 2019) , to Bayesian structure learning (Koller & Friedman, 2009; Heckerman et al., 1995; Daly et al., 2011; Chickering, 2002b) , causal discovery (Pearl, 1995; Tian & Pearl, 2001; Pearl, 2009; Bareinboim & Pearl, 2016; Peters et al., 2017) and how non-stationarity makes causal discovery easier (Zhang et al., 2017) .
Please see Appendix A for a longer discussion of related work.
We have established, in very simple bivariate settings, that the rate at which a learner adapts to sparse changes in the distribution of observed data can be exploited to infer the causal structure, and disentangle the causal variables.
This relies on the assumption that with the correct causal structure, those distributional changes are localized.
We have demonstrated these ideas through theoretical results, as well as experimental validation.
The source code for the experiments is available here:
This work is only a first step in the direction of causal structure learning based on the speed of adaptation to modified distributions.
On the experimental side, many settings other than those studied here should be considered, with different kinds of parametrizations, richer and larger causal graphs (see already Ke et al. (2019) based on a first version of this paper), or different kinds of optimization procedures.
Also, more work needs to be done in exploring how the proposed ideas can be used to learn good representations in which the causal variables are disentangled.
Scaling up these ideas would permit their application towards improving the way learning agents deal with non-stationarities, and thus improving sample complexity and robustness of these agents.
An extreme view of disentangling is that the explanatory variables should be marginally independent, and many deep generative models (Goodfellow et al., 2016) , and Independent Component Analysis models (Hyvärinen et al., 2001; Hyvärinen et al., 2018) , are built on this assumption.
However, the kinds of high-level variables that we manipulate with natural language are not marginally independent: they are related to each other through statements that are usually expressed in sentences (e.g. a classical symbolic AI fact or rule), involving only a few concepts at a time.
This kind of assumption has been proposed to help discover relevant high-level representations from raw observations, such as the consciousness prior (Bengio, 2017) , with the idea that humans focus at any particular time on just a few concepts that are present to our consciousness.
The work presented here could provide an interesting meta-learning approach to help learn such encoders outputting causal variables, as well as figure out how the resulting variables are related to each other.
In that case, one should distinguish two important assumptions: the first one is that the causal graph is sparse, which a common assumption in structure learning (Schmidt et al., 2007) ; the second is that the changes in distributions are sparse, which is the focus of this work.

assistant: text: The paper shows that a model with the correct underlying structure will adapt faster to a causal intervention than a model with the incorrect structure.