What is Causal Inference?
by Stephen M. Walker II, CoFounder / CEO
What is Causal Inference?
Causal inference is the process of determining the independent, actual effect of a particular phenomenon that is a component of a larger system. It is a widely studied interdisciplinary subfield of statistics, computer science, econometrics, epidemiology, psychology, and social sciences. The main challenge in causal inference is the fundamental problem of causal inference, which is that we do not observe both potential outcomes; we only observe one.
Key aspects of causal inference include:

Study Design — Causal inference is conducted via the study of systems where the measure of one variable is the cause and the measure of another variable is the effect.

Null Hypothesis — The first step of causal inference is to formulate a falsifiable null hypothesis, which is subsequently tested with statistical methods.

Statistical Methods — Frequentist statistical inference is used to determine the probability of an association, while Bayesian inference is used to determine the effect of an independent variable.

Causal Graphs — Causal graphs help in understanding and defining causal relationships, allowing researchers to reason about the world and make decisions based on causal effects.

Instrumental Variables — Instrumental variables are used to estimate the causal effect of one variable on another, helping to address potential confounding factors.
Causal inference has various applications, such as estimating drug or vaccine efficacy, understanding gene regulatory networks, and analyzing the impact of interventions on health outcomes. As AI and large language models (LLMs) continue to advance, causal inference techniques will play an increasingly important role in their development and application.
Key Concepts in Causal Inference
Concept  Definition 

Causal Models  Models that represent causeandeffect relationships and allow for the prediction of the effects of interventions. 
Counterfactuals  Statements about what would happen under different circumstances, used to reason about causal effects. 
DoCalculus  A set of rules developed by Judea Pearl for deriving the causal effect of an intervention from observational data. 
Potential Outcomes  A framework for causal inference that considers the possible outcomes of different interventions on the same unit. 
Some key details of causal inference in AI include:

Understanding Interventions — Causal inference helps in understanding what will happen if we intervene in a system, which is crucial for making informed decisions.

Predicting Outcomes — It allows for the prediction of outcomes in situations where controlled experiments are not possible, which is often the case in economics, social sciences, and medicine.

DataDriven Decisions — By understanding causality, AI can help policymakers and businesses make better datadriven decisions.
What are the key methods of Causal Inference in AI?
Causal inference in AI relies on several methods to establish causeandeffect relationships. Some of the key methods include:

Randomized Controlled Trials (RCTs) — Considered the gold standard for causal inference, RCTs randomly assign subjects to treatment or control groups to isolate the effect of the treatment.

Observational Studies with Propensity Score Matching — When RCTs are not feasible, observational studies can be used, where propensity score matching helps in creating comparable groups based on observed covariates.

Structural Causal Models (SCMs) — These models use graphs to represent causal relationships and are used to derive the consequences of interventions.

Granger Causality Tests — In time series data, Granger causality tests are used to determine if one variable can predict another variable's future values.
How does Causal Inference work in Machine Learning?
In machine learning, causal inference is used to understand the relationships between input variables and outcomes. It works by:

Modeling Causal Relationships — Using SCMs or other statistical models to represent the causal structure of the data.

Estimating Causal Effects — Applying methods like RCTs, docalculus, or instrumental variables to estimate the effect of an intervention.

Predicting Counterfactuals — Using the estimated models to predict what would happen under different scenarios, which is crucial for decisionmaking.
What are its benefits?
The benefits of incorporating causal inference in AI are significant:

Improved DecisionMaking — Causal models provide a deeper understanding of systems, leading to better decisions and policies.

Robust Predictions — Causal inference can lead to more robust predictions that hold under various conditions, not just the ones seen in the training data.

Ethical AI — Understanding causality can help in designing fairer AI systems by identifying and mitigating sources of bias.
What are the limitations of Causal Inference in AI?
Despite its potential, causal inference in AI faces several challenges:

Complexity of Causal Models — Building accurate causal models can be complex and requires domain expertise.

Data Quality and Availability — Causal inference requires highquality data, and the necessary data for establishing causality may not always be available.

Assumptions and Biases — Causal models are based on assumptions that, if incorrect, can lead to biased results.

Computational Intensity — Some causal inference methods are computationally intensive, making them challenging to apply in largescale problems.
Causal inference is a powerful tool in AI, but it must be applied carefully, considering its limitations and the quality of the underlying data. As AI continues to evolve, the integration of causal reasoning is likely to become increasingly important for developing intelligent systems that can understand and interact with the world in more humanlike ways.
Fundamental problem of causal inference
The "Fundamental Problem of Causal Inference" (FPCI) is that for any given unit, such as a patient in a medical study, we can only observe the outcome under treatment or control, not both. This limitation means we cannot directly compare the effects of treatment and control on the same unit. To address this, researchers use methods that make assumptions about the data and causal structure, such as random treatment assignment and the absence of unobserved confounders.
Frameworks like the Rubin causal model, Pearl's structural causal model, and structural equation modeling provide structured approaches to estimate causal effects despite the FPCI. These methods require careful scrutiny of their assumptions and an understanding of their limitations to ensure accurate causal inference.