Category: Kdd causal inference

Kdd causal inference

KDL studies how to construct causal models of complex systems, a fundamental research challenge at the frontier of machine learning.

Causal Inference Tutorial at KDD 2018

In particular, we create new methods, algorithms, and systems that infer causal dependence from observational and experimental data about complex and time-varying relationships among people, places, things, and events.

Current research focuses on several areas, including: 1 using causal models to provide human-understandable explanations of how deep neural networks make inferences; 2 using causal models to assess the competence of machine learning models the circumstances under which the models will perform well or poorly ; 3 learning causal models that provide accurate inferences when presented with novel inputs ; and 4 methods for effective evaluation of methods for causal modeling.

New developments in causal inference are vital because of growing interest in moving beyond simple predictive models, toward models that can correctly infer the effects of actions. Such models are critical to designing, managing, and understanding AI systems, the internet, cyber-physical systems, scientific communities, financial systems, social networks, complex software, and other types of complex systems.

Our research draws on concepts and techniques from a wide variety of technical communities, including machine learning, graphical models, probabilistic programming, statistics, experimental and quasi-experimental design, quantitative social science, database theory, complex adaptive systems, graph theory, and social network analysis. Our work intentionally spans the spectrum from foundational theory of statistical inference to large-scale empirical evaluation of the resulting algorithms and systems.GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.

If nothing happens, download GitHub Desktop and try again. If nothing happens, download Xcode and try again. If nothing happens, download the GitHub extension for Visual Studio and try again. Tools for graph structure recovery and dependencies are included. The package is based on Numpy, Scikit-learn, Pytorch and R. It implements lots of algorithms for graph structure recovery including algorithms from the bnlearnpcalg packagesmainly based out of observational data.

A tutorial is available here. For some additional functionalities, more libraries are needed for these extra functions and options to become available. Here is a quick install guide of the package, starting off with the minimal install up to the full installation.

As some of the key algorithms in the cdt package use the PyTorch package, it is required to install it. The package is then up and running! You can run most of the algorithms in the CausalDiscoveryToolbox, you might get warnings: some additional features are not available. Check out the package structure and more info on the package itself here.

In order to have access to additional algorithms from various R packages such as bnlearn, kpcalg, pcalg, Check out how to install all R dependencies in the before-install section of the travis. The r-requirements file notes all of the R packages used by the toolbox. Moreover, the hardware parameters are detected and defined automatically including number of GPUs, CPUs, available optional packages at the import of the package using the cdt. The whole package revolves around using the DiGraph and Graph classes from the networkx package.

Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together. Sign up. Package for causal inference in graphs and in the pairwise settings. Python R Other. Python Branch: master. Find file. Sign in Sign up. Go back. Launching Xcode If nothing happens, download Xcode and try again.For decades, causal inference methods have found wide applicability in the social and biomedical sciences.

As computing systems start intervening in our work and daily lives, questions of cause-and-effect are gaining importance in computer science as well. To enable widespread use of causal inference, we are pleased to announce a new software library, DoWhy.

In addition to providing a programmatic interface for popular causal inference methods, DoWhy is designed to highlight the critical but often neglected assumptions underlying causal inference analyses. DoWhy does this by first making the underlying assumptions explicit, for example, by explicitly representing identified estimands. And secondly by making sensitivity analysis and other robustness checks a first-class element of the causal inference process.

Our goal is to enable people to focus their efforts on identifying assumptions for causal inference, rather than on details of estimation. Our motivation for creating DoWhy comes from our experiences in causal inference studies over the past few years, ranging from estimating the impact of a recommender system to predicting likely outcomes given a life event.

In each of these studies, we found ourselves repeating the common steps of finding the right identification strategy, devising the most suitable estimator, and conducting robustness checks, all from scratch. While we were impressed—sometimes intimidated—by the amount of knowledge in causal inference literature, we found that doing any empirical causal inference remained a challenging task. Ensuring we understood our assumptions and validated them appropriately was particularly daunting.

We therefore asked ourselves, what if there existed a software library that provides a simple interface to common causal inference methods that codified best practices for reasoning about and validating key assumptions? Unlike in supervised learning, such counterfactual quantities imply that we cannot have a purely objective evaluation through a held-out test set, thus precluding a plug-in approach to causal inference.

For instance, for any intervention—such as a new algorithm or a medical procedure—one can either observe what happens when people are given the intervention, or when they are not. But never both. Therefore, causal analysis hinges critically on assumptions about the data-generating process. To succeed, it became clear to us that the assumptions need to be first-class citizens in a causal inference library.

We designed DoWhy using two guiding principles—making causal assumptions explicit and testing robustness of the estimates to violations of those assumptions.

First, DoWhy makes a distinction between identification and estimation. Identification of a causal effect involves making assumptions about the data-generating process and going from the counterfactual expressions to specifying a target estimand, while estimation is a purely statistical problem of estimating the target estimand from data.

Thus, identification is where the library spends most of its time, just like we commonly do in our projects.

For estimation, we provide methods based on the potential-outcomes framework such as matching, stratification and instrumental variables. A happy side-effect of using DoWhy is that you will realize the equivalence and interoperability of the seemingly disjoint graphical model and potential outcome frameworks.

Figure 1 — DoWhy. Separating identification and estimation of causal effect. Second, once assumptions are made, DoWhy provides robustness tests and sensitivity checks to test reliability of an obtained estimate. You can test how the estimate changes as underlying assumptions are varied, for example, by introducing a new confounder or by replacing the intervention with a placebo. Wherever possible, the library also automatically checks validity of obtained estimate based on assumptions in the graphical model.

Still, we also understand that automated testing cannot be perfect. DoWhy therefore stresses interpretability of its output; at any point in the analysis, you can inspect the untested assumptions, identified estimands if any and the estimate if any. Figure 2 — Causal inference in four lines. A sample run of DoWhy. In the future, we look forward to adding more features to the library, including support for more estimation and sensitivity methods and interoperability with available estimation software.

We welcome your feedback and contributions as we develop the library. You can check out the DoWhy Python library on Github. We include a couple of examples to get you started through Jupyter notebooks here.

The Mathematics of Causal Inference, with Reflections on Machine Learning and the Logic of Science

If you are interested in learning more about causal inference, do check our tutorial on causal inference and counterfactual reasoningpresented at KDD on Sunday, August 19th. Data platforms and analytics, Programming languages and software engineering, Security, privacy, and cryptography, Systems and networking. Episode February 19, - Dr. For him, thinking big involves what he calls thinking backwards, a framework of imagining the future, defining progress in reverse order and executing against landmarks along an uncertain path.Mobile phone usage provides a wealth of information, which can be used to better understand the demographic structure of a population.

In this paper, we focus on the population of Mexican mobile phone users. We first present an observational study of mobile phone usage according to gender and age groups.

We are able to detect significant differences in phone usage among different subgroups of the population. We then study the performance of different machine learning ML methods to predict demographic features namely, age and gender of unlabeled users by leveraging individual calling patterns, as well as the structure of the communication graph. We show how a specific implementation of a diffusion model, harnessing the graph structure, has significantly better performance over other node-based standard ML methods.

We provide details of the methodology together with an analysis of the robustness of our results to changes in the model parameters. Furthermore, by carefully examining the topological relations of the training nodes seed nodes to the rest of the nodes in the network, we find topological metrics which have a direct influence on the performance of the algorithm. This is a preview of subscription content, log in to check access. Rent this article via DeepDyve. The interquartile range IQR is a measure of statistical dispersion.

We note that in all eigenvectors, the logarithmic version of the variables got systematically higher coefficients than the plain variables, which is expectable since they have higher variance. For the net duration of calls, we considered only users who had both incoming and outgoing calls.

Tutorial on Causal Inference and Counterfactual Reasoning

Adali S, Golbeck J Predicting personality with social behavior: a comparative study. Soc Netw Anal Min 4 1 :1— Cambridge University Press, Cambridge. Blumenstock J, Eagle N Mobile divides: gender, socioeconomic status, and mobile phone use in Rwanda.

Demographics of mobile phone use in Rwanda. Transportation —5. Distribution, social theory and duration prediction. Soc Netw Anal Min 3 3 — J Mach Learn Res — Feld SL Social structural determinants of similarity among associates. Am Sociol Rev — Free Press, New York. Artificial Intelligence for Development.Traditionally, causal relationships are identified by making use of interventions or randomized controlled experiments. However, conducting such experiments is often expensive or even impossible due to cost or ethical concerns.

Therefore there has been an increasing interest in discovering causal relationships based on observational data, and in the past few decades, significant contributions have been made to this field by computer scientists.

Inspired by such achievements and following the success of CDCDand CDCD continues to serve as a forum for researchers and practitioners in data mining and other disciplines to share their recent research in causal discovery in their respective fields and to explore the possibility of interdisciplinary collaborations in the study of causality. All submitted papers will be reviewed and selected by the program committee on the basis of originality, technical quality, relevance to the workshop and presentation quality.

When Aug 5, - Aug 5, May 5, Jun 1, Jul 1, Call For Papers. Advances in Social Networks Analysis and Mining.Conventional machine learning methods, built on pattern recognition and correlational analyses, are insufficient for causal analysis. This tutorial will introduce participants to concepts in causal inference and counterfactual reasoning, drawing from a broad literature on the topic from statistics, social sciences and machine learning.

We first motivate the use of causal inference through examples in domains such as recommender systems, social media datasets, health, education and governance. To tackle such questions, we will introduce the key ingredient that causal analysis depends on—counterfactual reasoning—and describe the two most popular frameworks based on Bayesian graphical models and potential outcomes.

Based on this, we will cover a range of methods suitable for doing causal inference with large-scale online data, including randomized experiments, observational methods like matching and stratification, and natural experiment-based methods such as instrumental variables and regression discontinuity. We will also focus on best practices for evaluation and validation of causal inference techniques, drawing from our own experiences. We show application of these techniques through Jupyter notebooks, demonstrating how core concepts translate to empirical work.

Throughout, we emphasise considerations of working with large-scale data from online systems, such as logs of user interactions or social data. The goal of this tutorial is to help you understand the basics of causal inference, be able to appropriately apply the most common causal inference methods, and be able to recognize situations where more complex methods are required. This site uses cookies for analytics, personalized content and ads.

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kdd causal inference

Sections Introduction : Patterns and predictions are not enough Methods : Conditioning-based methods and natural experiments Considerations : Special considerations with large-scale and network data Broader Landscape : Heterogeneous treatment effects, machine learning and causal discovery References : Further reading.

Groups Causality and Machine Learning. Research Areas Artificial intelligence.SIGKDD promotes basic research and development in KDD, adoption of "standards" in the market in terms of terminology, evaluation, methodology and interdisciplinary education among KDD researchers, practitioners, and users.

Many researchers have attended the conference from various industries. This talk is from JD. This talk covered end to end activities in retail sector with a strong focus on AI and promoting Retail-as-a-Service as a solution to offer customers more intimate and personalized shopping experience.

Using multiple sources of customers data like online transaction data, click through, social media, offline data; they developed algorithms to find Behavior description and intention detection.

But, did not cover any exclusive details of exact approach on this.

kdd causal inference

Explained their approach on customer demand on a product. Understood the seq2seq deep learning model and how to take advantage of this model with huge big data. Feature are developed to cover all possible data dimensions like sales data, Sku attributes, Time and Location. Challenging part mentioned in the talk is highly non stationary time series based on high variability of customer demand.

Covered a case study about probabilistic prediction for each sku every day. Content based methods, Collaborative filtering methods User-User, Item-Item collaboration; methods - Association rule, Probabilistic model based, nearest neighbor memory based, matrix factorizationHybrid methods are used in product recommendation algorithms. Covered the usage of multi arm bandits. Deterministic NP hard discrete optimization problem is solved to minimize the number of locally missed orders.

Demand estimation with MLE is covered in Replenishment problem. Knapsack to solve inventory level; goal is to maximize the revenue under capacity constraint. Case study of 7 fresh grocery customer's dynamic pricing and product tracking. Unique customer is pointed based on facial recognition technology used inside a store; customer shopping path inside the store is tracked to enrich the data set. Discussed about how machine learning methods today focus on correlation analyses and prediction, and how this is insufficient when we need to understand causal mechanisms and design interventions.

Covered some scenarios where such correlations and predictive analyses can fail, showing a special case phenomenon called Simpson's Paradox. Spoke about 3 layer causal hierarchy Association, Intervention and Counterfactual. Covered the concept of auditing the effect of an algorithm and usage of randomized experiments for causal inference. Based on Markov assumption, a structural causal model framework for expressing complex causal relationships.

kdd causal inference

Structural causal model framework is a Microsoft project. Causal inference knowledge is used in ranking features or identifying the dependent relationship among features. This talk is about a generic framework for predicting E-tail product return named HyperGo. For a given basket, they propose a local graph cut algorithm using truncated random walk on the hyper graph to identify similar historical baskets.

Based on these baskets, HyperGo is able to estimate the return intention on two levels: basket-level vs. One major benefit of the proposed local algorithm lies in its time complexity, which is linearly dependent on the size of the output cluster and poly logarithmic dependent on the volume of the hypergraph. This makes HyperGo particularly suitable for processing large-scale data sets. The experimental results on multiple real-world E-tail data sets demonstrate the effectiveness and efficiency of HyperGo.

This talk is about the extraction of missing attribute values is to find values describing an attribute of interest from a free text input.


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