Significance of Causal Inference in Research

A/B testing is a staple in the industry, often highlighted as the gold standard for experimentation. But how often do we talk about causal analysis, the broader and equally important field that underpins it? While it may be less commonly referenced, causal analysis is fundamental to answering deeper questions about cause-and-effect relationships in data. This introductory blog by a Microsoft data scientist provides a clear and approachable overview of causal analysis, breaking down its major components and their applications. Broadly, causal analysis can be categorized into two key areas:   -- Causal Discovery: This focuses on identifying the underlying causal structure from data. It answers questions like, "What factors influence an outcome, and how are they connected?" Algorithms like the Peter-Clark algorithm and Greedy Equivalence Search help uncover these relationships, often represented as causal graphs.   -- Causal Inference: This focuses on quantifying the effect of one variable on another. It answers questions like, "How much does X cause Y?" Techniques range from experimental approaches like A/B testing to observational methods like propensity score matching, instrumental variables, and difference-in-differences. Our commonly known A/B testing is a subset of causal inference and relies on controlled experiments to estimate effects. However, non-experimental approaches offer powerful alternatives, especially when experiments aren’t feasible. If you’re curious about expanding your understanding of causality and its practical applications, this blog is a great starting point to explore how causal analysis can elevate data-driven decision-making. #datascience #analytics #causal #discovery #inference #abtest  – – –  Check out the "Snacks Weekly on Data Science" podcast and subscribe, where I explain in more detail the concepts discussed in this and future posts:    -- Spotify: https://lnkd.in/gKgaMvbh   -- Apple Podcast: https://lnkd.in/gj6aPBBY    -- Youtube: https://lnkd.in/gcwPeBmR https://lnkd.in/gfxTjapV

Causal inference makes evaluating algorithms tricky because standard data splitting techniques don’t work as usual. Cross validation can tell you if you are predicting well, but not necessarily that you are estimating cause-effect relationships correctly, precisely because the data you have in hand may be subject to confounding. To take a textbook example, ice cream sales may well predict electricity bills, but only because both are positively associated with warm weather. With routine validation techniques unavailable, it becomes extra important to stress-test causal inference methods with simulated data, where the true causal mechanisms are known. To really spice things up, you can have a collaborator make up the data for you and then try to estimate the treatment effects and see how well your method does, compared to others. This is the idea behind causal inference data challenges. In 2017, I was in charge of putting together the data sets for ACIC. Since then, aggressive pre-testing has been my go-to tool for developing machine learning methods for causal inference. Here are some lessons I’ve learned in the process: 1. Realistic causal inference is really hard. What seems like an easy data generating process (DGP) can make it impossible to effectively learn treatment effects. Realistic levels of noise obscure patterns and make the necessary sample sizes much larger than you’d expect. 2. It’s easy to accidentally cheat. If you restrict yourself to linear models or single-variable models or only weak confounding, the problem can seem easier than it actually is. Causal diagrams are very useful for synthesizing interesting and plausible confounding structures because you can have many variables that inter-relate in non-trivial ways, only some of which are available to the analyst. 2*. One way that people cheat in the heterogeneous treatment effect (HTE) literature is to simulate effect heterogeneity that is larger in magnitude than any other prognostic factors and to allow sign changes as well -- the treatment can not only affect individuals differently, but it is the single biggest influence on the outcome and it can have the opposite effect for some individuals. 3) Some asymptotic results are nothing but dirty lies. Methods published in prestigious journals and which are widely cited can fail miserably in simulation studies. Bear in mind that frequentist guarantees are supposed to hold for *any* data generating process, so getting 50% coverage when you’re supposed to get 95% on *any* simulated data is proof that the asymptotic approximation is poor. I talk about these and related issues in a Bayesian Analysis webinar from 2020: https://lnkd.in/g-2ckK2S Our method Bayesian Causal Forests (BCF) is a BART-based supervised-learning method specifically engineered for realistic treatment effect estimation. It is a core component of our #stochtree package (for R and python).

*** Backdoor Criterion: Cornerstone of Causal Inference *** Let’s examine the backdoor criterion, a critical concept in causal inference. Imagine you are tasked with estimating the causal effect of a variable X—think of it as a treatment or intervention—on an outcome Y, which represents the effect or result you are observing. However, this direct relationship can become muddled by the influence of other variables, known as confounders, that may distort the connection between X and Y. The backdoor criterion offers a structured approach to pinpoint which variables you need to adjust for to block these confounding influences and accurately isolate the genuine causal effect. In more straightforward terms, the process begins with constructing a graphical representation of your hypotheses about how the various variables interrelate. This graph is a visual map, showcasing the connections and potential influences among X, Y, and related factors. Next, you will specifically search for what are known as “backdoor paths.” These indirect routes lead from X to Y but run counter to the direction of causality, often looping back into X through other variables. By identifying these pathways, you can better understand how different factors might create a false impression of the relationship between X and Y. A particular set of variables, denoted as Z, must be isolated to satisfy the backdoor criterion. When you control for these variables, you effectively block all non-causal paths from X to Y, ensuring that the actual effect of X on Y can be observed without interference from these external influences. It’s like carefully pruning a tangled vine—removing the extraneous growth to allow unimpeded the essential branch of causality to flourish. In the visual representation of this concept, you will typically find three pivotal variables at the center: - X: the cause or treatment that you are investigating - Y: the effect or outcome that you hope to measure - Z: the potential confounders that may distort the relationship between X and Y Without controlling for Z, the backdoor path creates a deceptive association between X and Y, leading to the erroneous conclusion that variations in Y are influenced by X when, in reality, they may stem from the influence of Z. By adjusting for Z, you effectively “close” the backdoor, allowing you to reveal the authentic causal connection from X to Y. This adjustment is crucial in ensuring that your analysis accurately reflects the causal dynamics at play, free from the distortions caused by common confounding factors. See the post image. --- B. Noted

The Transformative Impact of Causal Inference in Unconventional Oil and Gas The shift from traditional association-based models to causal inference represents a pivotal advancement in our understanding of complex systems. A poignant example of this necessity is the prolonged scientific debate over the causal link between smoking and cancer. Here, traditional statistical frameworks stumbled, primarily because they lacked the language to assert causality rather than mere association. This paradigm is echoed within the oil and gas industry, where we've historically leaned on associational models. These models, for all their utility, fall short when it comes to predicting outcomes in unconventional wells—requiring a leap from observed phenomena to counterfactual scenarios, each governed by its unique probability distribution. Ergo, the significance of this leap cannot be overstated. Our engagement with our first client underscored this point not as a critique of past practices but as an enlightening journey towards a deeper understanding and optimization. By clarifying the distinction between association and causation, we illuminated a path forward that was previously veiled by the confines of conventional thinking. The result was not merely an academic exercise but a practical revelation, culminating in over a billion dollars in savings for our client. This venture into causal inference does more than refine our analytical tools; it marks a philosophical shift towards asking more profound questions and deriving more precise, actionable insights.

📊 Bringing Rigor to Real-World Evidence with ML-Assisted Causal Inference A recent study in Int J Med Inform (2024) evaluated the "impact of self-help groups on treatment completion in over 150,000 adults with opioid use disorder receiving MOUD". The result? A 26% higher completion rate among those who also attended self-help programs. 🏆 But what truly sets this study apart is its #methodology. - Instead of relying on traditional regression, it used a machine learning (#ML) assisted causal inference framework: 🧠 Outcome Adaptive Elastic Net (OAENet) to identify true #confounders 🤖 ML-based propensity scores (#PSM) (Random Forest, XGBoost) for flexible adjustment 📉 ATT estimation + McNemar’s test to validate causal effect 🔎 Lasso regression to identify high-benefit patient subgroups This approach offers greater robustness, better covariate balance, and improved insights, pushing real-world evidence closer to trial-like rigor. This is a great example of how causal inference and ML can work together in public health and outcomes research. 📖 Read full study here: https://lnkd.in/gCQukqaw #RealWorldEvidence #CausalInference #MachineLearning #Biostatistics #HealthDataScience #OUD #EconML #DoWhy #CausalInference #epidemiology #biostatistics #RealWorldEvidence

Want to be a better researcher? Start with a pen. One of the most powerful things I teach in data analysis and UX research is to draw a causal diagram before collecting a single data point. These diagrams, or DAGs, help you: * map out the real-world forces shaping behavior * reveal hidden confounders and biases * choose the right variables to measure and adjust for In UX, user behavior is messy. Context matters. Motivation matters. Timing matters. If you ignore these, you risk building the wrong product — or running an experiment you can’t trust. By sketching out your causal assumptions early, you make your reasoning explicit and transparent, which helps your team stay aligned. And you don’t need anything fancy — a whiteboard or sticky note is enough to catch blind spots before they cost you. Draw before you analyze. It’s one of the simplest ways to do better, clearer, more human-centered research. #UXResearch #CausalInference #DataAnalysis #UserCenteredDesign #ResearchMethods

Exploring Causal Inference and Bayesian Structural Time Series (BSTS) in Product Analytics: In complex product ecosystems, understanding causal impact—rather than simple correlation—is critical for driving strategic decisions. Recently, I’ve been focusing on the application of causal inference and Bayesian Structural Time Series (BSTS) to address this very challenge, particularly in non-experimental, observational environments where randomized testing isn’t always feasible. A core research direction for me has been unpacking the causal dynamics between upstream and downstream metrics—for instance: 1. What is the causal impact of revenue earned through product usage on future user engagement? 2. Can shifts in engagement behavior serve as early indicators for changes in monetization or retention? 3. How do we adjust for time-varying confounders, identify structural breaks, and produce credible counterfactual forecasts post-intervention? These are not just statistical questions—they're strategic levers for organizations that want to optimize product health, pricing models, and growth initiatives with precision. By leveraging BSTS alongside techniques like synthetic controls and hierarchical modeling, we can model latent structures, account for uncertainty, and extract directional insights from noisy, high-dimensional time series data. There’s still a lot of open ground around identifiability, prior specification, and multi-metric causal structures—and I’m keen to connect with others who are working on advanced causal ML frameworks or deploying BSTS in production environments. If you’re working in this space (or thinking about integrating causal reasoning into product or business strategy), let’s connect! #CausalInference #BayesianStatistics #BSTS #TimeSeriesModeling #CausalML #ProductAnalytics #DataScienceLeadership #SyntheticControl #ObservationalData #DecisionScience #TechStrategy

The document Causal Inference in Econometrics presents a structured overview of the key methods used to estimate causal effects in econometric analysis. It begins with the potential outcomes framework, which lays the foundation for understanding causality by emphasizing the core challenge: for each individual, we can only observe one potential outcome, not both the treated and untreated states. To address this, randomized experiments are introduced as the gold standard, as they eliminate selection bias and allow for clear identification of causal effects. However, in the more common setting of observational data, researchers must rely on alternative strategies, such as selection-on-observables, which controls for confounding variables, or instrumental variables, which leverage external sources of variation to mimic randomization and uncover causal relationships. The text also covers regression techniques, difference-in-differences, and regression discontinuity designs, each useful under specific assumptions. It discusses both average treatment effects and heterogeneous effects, extending to quantile treatment effects. Finally, it includes mathematical foundations necessary to understand and apply these methods rigorously. Link: https://lnkd.in/e-DWDfKb #statistics #causalinference #econometrics

🌟 🌐 The data alchemy: Integrating Causal Inference with Knowledge Graphs 🧠  🧩 In the world of data, knowing what is happening is just the beginning. The real power lies in understanding why it happens. Enter causal inference—the game-changer in data science that digs beneath surface-level correlations to unveil true cause-and-effect relationships. What is Causal Inference? 🤔 Imagine you're running a marketing campaign and see a spike in sales. Great, right? But is that spike really due to your campaign, or are there hidden factors at play? Causal inference helps you answer this crucial question by identifying whether one event directly influences another. Enter Knowledge Graphs (KGs) 🌐 Knowledge Graphs are like sophisticated maps for data. They connect entities (like people or products) with relationships (like "bought by" or "works for"), offering a structured way to visualize complex connections. Think of it as bringing order to the chaos of raw data. The Power Couple: Causal Inference + KGs 💥 When you combine the detective work of causal inference with the structured brilliance of Knowledge Graphs, magic happens: ➢ Ask Better Questions: Move beyond "what" is happening to "why" it's happening. KGs enriched with causal insights can answer these deeper questions. ➢ Smarter Decisions: Businesses can pinpoint root causes, driving actions that lead directly to desired outcomes. ➢ Robust Insights: In healthcare, for example, this duo can uncover the root causes of diseases, leading to better treatments and outcomes. Meet CareKG—Your Causality sidekick 🦸♂️ CareKG is a pioneering framework that brings this dream team to life. It integrates causal relationships into KGs, considering rich metadata like class subsumption and integrity constraints. With CareKG, your data isn't just interconnected—it's insightful. Why you should care 🧐 ➢ Healthcare: Identify the real causes behind patient outcomes for better medical interventions. ➢ Business Intelligence: Understand which marketing strategies truly drive sales. ➢ Social Media: Discover why certain content goes viral and its impact on public opinion. #DataScience #AI #MachineLearning #KnowledgeGraphs #CausalInference #Innovation #BigData #Tech #ArtificialIntelligence #BeAvailable

The alternative to causal inference is worse causal inference. I've written about this before (link in comments), but I was reminded of it again recently. Sometimes data scientists balk at how long we have to run an A/B test to get enough statistical power for reliable insights. (Google folks might be wondering what I'm talking about, but not every company gets billions of experimental units each day in the form of search queries.) And so they turn to their rich observational data. They say they get much stronger results (ie smaller p-values, narrower confidence intervals) from simple correlational studies than from A/B tests. It's an illusion. Even when adjusting for observed confounders, an observational study involves uncertainty from the possible influence of unobserved differences between treatment and control. This additional uncertainty is not reflected in p-values or confidence intervals. It doesn't make sense to compare the p-values you get from an A/B test with the p-values you get from an observational study. They are measuring different things. I am all for leveraging the enormous observational datasets many tech companies possess. Those datasets are some of our most valuable assets. But we need to be clear-eyed about the reliability of such insights. We can explore this additional uncertainty using methods like sensitivity analysis. I recently wrote about this methodology on my blog. Link in comments! Sensitivity analysis doesn't make observational studies as reliable as randomized experiments, but it goes a long way towards closing that gap. #causalinference #abtesting #datascience