Error Causal inference for Multi-Fusion models (2021)

Abstract: In this paper, we propose an error causal inference method that could be used for finding dominant features for a faulty instance under a well-trained multi-modality input model, which could apply to any testing instance. We evaluate our method using a well-trained multi-modalities stylish caption generation model and find those causal inferences that could provide us the insights for next step optimization.

## introduction as machine learning models become more complex and training data become bigger, it is harder for humans to find errors manually once some output went wrong. this problem is exacerbated by the black box nature of most machine learning models. when a model fails, it can be difficult to determine where the error comes from. this is especially true in problems that are inherently multimodal, such as image captioning, where often multiple models are combined together in order to produce an output. this lack of transparency or ability to perform a vulnerability analysis can be a major hindrance to machine learning practitioners when faced with a model that doesn't perform as expected. recently, more and more people begin to fuse text and visual information for downstreaming task. in many cases, these models utilize specialized, pre-trained models to extract features. in these situations, it is highly likely that the source of these errors is from these pre-trained networks either being misused or not being interpreted correctly by the larger machine learning architecture. in this paper, we explore how one would perform a vulnerability analysis in these situations. specifically, we are interested in identifying model errors likely caused by these pre-trained networks. specifically, we aim to diagnose these errors by systematically removing elements of the larger machine learning model to pinpoint what the causes of errors happen to be. this is especially critical in tasks that utilize multi-modality input models since often these models utilize attention. if the model attends to the wrong features, then this error could potentially cascade throughout the network. in other words, we seek to answer the question, "given a trained model m which has input features x, y, z, if the current test example is not performing well, is that because of the given features or not? if it is, which specific feature is more likely to blame?" by answering this question, we can give machine learning practitioners, specifically those who are inexperienced with machine learning and ai concepts, some direction in how to improve the performance of their architecture. we summarize our contributions as follows: 1. we provide a practical method to discover causal errors for multi-modality input ml models; 2. we explore how this method can be applied to state-of-the-art machine learning models for performing stylish image captioning; 3. evaluate our method by through a case study in which we assess whether we can improve the performance of the investigated instance by removing or replacing these problematic features. ## related work our approach to sourcing these errors uses causal inference @xcite . in this section, we will review works related to causal inference as well as works that provided the inspiration for this paper. invariance principle invariance principle has been used for finding general causal for some outcome under designed treatment process, where people desired to find actual effect of a specific phenomenon. invariant causal prediction @xcite has been proposed to offer an practical way to find casuals under linear model assumption. it later got extended to nonlinear model and data @xcite . this invariance can be roughly phrased as the outcome y of some model m would not change due to environment change once given the cause for this y . an example of an environment change when @xmath0, z, w ) and the cause for y is x, could be a change on z or w . the invariance principle has been popularly used in machine learning models to train causal models @xcite . we are going to employ the same insight, using the invariance principle to find cause in our paper but landing in different perspectives. we are not intended to train a model, instead, we are going to use the well-trained models to derive the source cause for lower performance instances. potential outcome and counterfactual. (rubin, 2005) proposed using potential outcomes for estimating causal effects. potential outcomes present the values of the outcome variable y for each case at a particular point in time after certain actions. but usually, we can only observe one of the potential outcome since situations are based on executing mutually exclusive actions (e.g. give the treatment or don't give the treatment). the unobserved outcome is called the "counterfactual" outcome. in this paper we can observe the counterfactual by removing certain input features from the language generation based on multi-input task. debugging errors caused by feature deficiencies this paper is also related to debugging errors from input. while we are more focus on using a causal inference way to get the real cause for low performance rather than only exploring associations @xcite ## methodology the goal of this paper is to perform a causal analysis in order to determine the likely source of errors in a well-trained model. in the following sections, we will outline our underlying hypotheses related to this task and go into details on the task itself. ## experiment to show the effectiveness of our approach, we will examine its performance on a stylish image captioning task that uses multi-modality feature fusion. while we focus on this task as an example, this approach could be applied to many other domains. ## result and discussion the result is shown in the ## conclusion in this paper, we apply an extended invariance principle to provide a method for performing error causal inference. we evaluate our method under on a stylish image captioning model that uses multimodal fusion in its input features. we show that we could improve the performance of this model based on simply removing or replacing those found causal errors. over 70% of the predicted errors could be modified to improve performance. also, our method is model-agnostic, it could be used for different fusion model for optimization, debugging or assessing purpose.