Targeted Maximum Likelihood Estimation (TMLE) (also more accurately referred to as Targeted Minimum Loss-Based Estimation) is a general statistical estimation framework for causal inference and semiparametric models. TMLE combines ideas from maximum likelihood estimation, semiparametric efficiency theory, and machine learning. It was introduced by Mark J. van der Laan and colleagues in the mid-2000s as a method that yields asymptotically efficient plug-in estimators while allowing the use of flexible, data-adaptive algorithms such as ensemble machine learning for nuisance parameter estimation. TMLE is used in epidemiology, biostatistics, and the social sciences to estimate causal effects in observational and experimental studies. Applications of TMLE include Longitudinal TMLE (LTMLE) for time-varying treatments and confounders. Variations in how the targeting step in TMLE is carried out have resulted in various versions of TMLE such as Collaborative TMLE (CTMLE) and Adaptive TMLE for improved finite-sample performance and automated variable selection. == History == The TMLE framework was first described by van der Laan and Rubin (2006) as a general approach for the construction of efficient plug-in estimators of smooth features of the data density. It was demonstrated in the context of causal inference and missing data problems. It was developed to address limitations of traditional doubly robust methods, such as Augmented Inverse Probability Weighting (AIPW), by respecting the plug-in principle in the sense that it respects that the target parameter is a function of the data density that is an element of the statistical model. TMLE estimates the data density or relevant parts of it with machine learning and targets these machine learning fits before it is plugged in the target parameter mapping. In this manner, a TMLE always respects global knowledge and satisfies known bounds such as that the target parameter is a probability . Since its introduction, TMLE has been developed in a series of theoretical and applied papers, culminating in book-length treatments of the method and its applications to survival analysis, adaptive designs, and longitudinal data. == Methodology == At its core, TMLE is a two-step estimation procedure: Initial estimation: Machine learning methods (such as the Super Learner ensemble) are used to obtain flexible estimates of nuisance parameters, such as outcome regressions and propensity scores. Targeting step: The initial estimate is updated by solving a score equation (the efficient influence function) so that the final estimator is consistent, asymptotically normal, and efficient under mild regularity conditions. The targeted machine learning fit is then mapped into the corresponding estimator of the target parameter by simply plugging it in the target parameter mapping. This approach balances the bias–variance trade-off by combining data-adaptive estimation with semiparametric efficiency theory. TMLE is doubly robust, meaning it remains consistent if either the outcome model or the treatment model is consistently estimated. === Formula === Here we explain the TMLE of the average treatment effect of a binary treatment on an outcome adjusting for baseline covariates. Consider i.i.d. observations O i = ( W i , A i , Y i ) {\displaystyle O_{i}=(W_{i},A_{i},Y_{i})} from a distribution P 0 {\displaystyle P_{0}} , where W {\displaystyle W} are baseline covariates, A {\displaystyle A} is a binary treatment, and Y {\displaystyle Y} is an outcome. Let Q ¯ ( a , w ) = E [ Y ∣ A = a , W = w ] {\displaystyle {\bar {Q}}(a,w)=\mathbb {E} [Y\mid A=a,W=w]} represent the outcome model and g ( a ∣ w ) = P ( A = a ∣ W = w ) {\displaystyle g(a\mid w)=P(A=a\mid W=w)} represent the propensity score. The average treatment effect (ATE) is given by ψ 0 = E { Q ¯ ( 1 , W ) − Q ¯ ( 0 , W ) } . {\displaystyle \psi _{0}=\mathbb {E} \{{\bar {Q}}(1,W)-{\bar {Q}}(0,W)\}.} A basic TMLE for the ATE proceeds as follows: Step 1: Estimate initial models. Obtain estimates Q ¯ ^ ( a , w ) {\displaystyle {\hat {\bar {Q}}}(a,w)} and g ^ ( a ∣ w ) {\displaystyle {\hat {g}}(a\mid w)} , often using flexible methods such as Super Learner. Step 2: Compute the clever covariate. Define: H ( A , W ) = A g ^ ( 1 ∣ W ) − 1 − A g ^ ( 0 ∣ W ) . {\displaystyle H(A,W)={\frac {A}{{\hat {g}}(1\mid W)}}-{\frac {1-A}{{\hat {g}}(0\mid W)}}.} Step 3: Estimate the fluctuation parameter. Fit a logistic regression of Y {\displaystyle Y} on H ( A , W ) {\displaystyle H(A,W)} with logit ( Q ¯ ^ ( A , W ) ) {\displaystyle \operatorname {logit} ({\hat {\bar {Q}}}(A,W))} as offset. This yields ε ^ {\displaystyle {\hat {\varepsilon }}} , the MLE that solves the score equation: 1 n ∑ i = 1 n H ( A i , W i ) { Y i − Q ¯ ^ ε ( A i , W i ) } = 0. {\displaystyle {\frac {1}{n}}\sum _{i=1}^{n}H(A_{i},W_{i}){\big \{}Y_{i}-{\hat {\bar {Q}}}^{\varepsilon }(A_{i},W_{i}){\big \}}=0.} Step 4: Update the initial estimate. Apply the "blip" to obtain the targeted estimate: Q ¯ ^ ∗ ( A , W ) = expit ( logit ( Q ¯ ^ ( A , W ) ) + ε ^ H ( A , W ) ) . {\displaystyle {\hat {\bar {Q}}}^{}(A,W)=\operatorname {expit} {\Big (}\operatorname {logit} {\big (}{\hat {\bar {Q}}}(A,W){\big )}+{\hat {\varepsilon }}\,H(A,W){\Big )}.} Step 5: Compute the TMLE. The ATE estimate is: ψ ^ TMLE = 1 n ∑ i = 1 n [ Q ¯ ^ ∗ ( 1 , W i ) − Q ¯ ^ ∗ ( 0 , W i ) ] . {\displaystyle {\hat {\psi }}_{\text{TMLE}}={\frac {1}{n}}\sum _{i=1}^{n}{\big [}{\hat {\bar {Q}}}^{}(1,W_{i})-{\hat {\bar {Q}}}^{}(0,W_{i}){\big ]}.} Inference. The efficient influence function (EIF) for the ATE is: D ∗ ( O ) = H ( A , W ) { Y − Q ¯ ∗ ( A , W ) } + Q ¯ ∗ ( 1 , W ) − Q ¯ ∗ ( 0 , W ) − ψ . {\displaystyle D^{}(O)=H(A,W)\{Y-{\bar {Q}}^{}(A,W)\}+{\bar {Q}}^{}(1,W)-{\bar {Q}}^{}(0,W)-\psi .} The variance is estimated by σ ^ 2 = n − 1 ∑ i = 1 n ( D ∗ ( O i ) ) 2 {\displaystyle {\hat {\sigma }}^{2}=n^{-1}\sum _{i=1}^{n}{\big (}D^{}(O_{i}){\big )}^{2}} , yielding Wald-type confidence intervals ψ ^ TMLE ± z 1 − α / 2 σ ^ / n {\displaystyle {\hat {\psi }}_{\text{TMLE}}\pm z_{1-\alpha /2}\,{\hat {\sigma }}/{\sqrt {n}}} . Remark. For continuous outcomes, a linear fluctuation Q ¯ ^ ∗ = Q ¯ ^ + ε ^ H {\displaystyle {\hat {\bar {Q}}}^{}={\hat {\bar {Q}}}+{\hat {\varepsilon }}\,H} may be used instead. For bounded continuous outcomes, the logistic fluctuation (after rescaling Y {\displaystyle Y} to [ 0 , 1 ] {\displaystyle [0,1]} ) is often preferred for improved finite-sample performance. == Applications == TMLE has been applied in: Epidemiology: Estimating causal effects of exposures and interventions in observational cohort studies. Clinical trials and real-world evidence: The Targeted Learning roadmap provides a structured framework for generating and validating real-world evidence (RWE), bridging randomized trials and observational data using TMLE and related estimation techniques. This approach enables transparency, sensitivity analysis, and stronger causal inference for regulatory and clinical trial contexts. High-dimensional settings: Integration with ensemble methods for causal effect estimation. TMLE has been successfully applied in pharmacoepidemiology where a large number of covariates are automatically selected to adjust for confounding. In a study of post–myocardial infarction statin use and 1-year mortality, TMLE demonstrated robust performance relative to inverse probability weighting in scenarios with hundreds of potential confounders. == Derivatives and extensions == Longitudinal TMLE (LTMLE): A methodological extension of TMLE for longitudinal data with time-varying treatments, confounders, and censoring. It allows the estimation of dynamic treatment regimes and intervention-specific causal effects over time. This framework was originally introduced by van der Laan & Gruber (2012). Collaborative TMLE (CTMLE): Enhances finite-sample performance and variable selection by collaboratively fitting the treatment mechanism in conjunction with the target parameter. == Software == Several R packages implement TMLE and related methods: tmle: Functions for binary, categorical, and continuous outcomes. ltmle: Implementation for longitudinal data with time-varying treatments and outcomes. ctmle: Algorithms for collaborative TMLE and adaptive variable selection. SuperLearner: A theoretically grounded, cross-validated ensemble learning method that combines predictions from multiple algorithms to minimize predictive risk. Widely used in TMLE for estimating nuisance parameters. The original implementation is available as the R package SuperLearner. Recent machine learning platforms like H2O AutoML implement similar ensemble strategies, combining diverse learners in parallel and leveraging stacking and blending techniques, effectively functioning as a large-scale Super Learner.
FoundationDB
FoundationDB is a free and open-source multi-model distributed NoSQL database owned by Apple Inc. with a shared-nothing architecture. The product was designed around a "core" database, with additional features supplied in "layers." The core database exposes an ordered key–value store with transactions. The transactions are able to read or write multiple keys stored on any machine in the cluster while fully supporting ACID properties. Transactions are used to implement a variety of data models via layers. The FoundationDB Alpha program began in January 2012 and concluded on March 4, 2013, with their public Beta release. Their 1.0 version was released for general availability on August 20, 2013. On March 24, 2015, it was reported that Apple has acquired the company. A notice on the FoundationDB web site indicated that the company has "evolved" its mission and would no longer offer downloads of the software. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license. == Main features == The main features of FoundationDB include the following: Ordered key–value store In addition to supporting standard key-based reads and writes, the ordering property enables range reads that can efficiently scan large swaths of data. Transactions Transaction processing employs multiversion concurrency control for reads and optimistic concurrency for writes. Transactions can span multiple keys stored on multiple machines. ACID properties FoundationDB guarantees serializable isolation and strong durability via redundant storage on disk before transactions are considered committed. Layers Layers map new data models, APIs, and query languages to the FoundationDB core. They employ FoundationDB's ability to update multiple data elements in a single transaction, ensuring consistency. An example is their SQL layer. Commodity clusters FoundationDB is designed for deployment on distributed clusters of commodity hardware running Linux. Replication FoundationDB stores each piece of data on multiple machines according to a configurable replication factor. Triple replication is the recommended mode for clusters of 5 or more machines. Scalability FoundationDB is designed to support horizontal scaling through the addition of machines to a cluster while automatically handling data replication and partitioning. Systems supported FoundationDB supports packages for Linux, Windows, and macOS. The Linux version supports production clusters, while the Windows and macOS versions support local operation for development purposes. Configurations on Amazon EC2 are also supported. Programming language bindings FoundationDB supports language bindings for Python, Go, Ruby, Node.js, Java, PHP, and C, all of which are made available with the product. == Design limitations == The design of FoundationDB results in several limitations: Long transactions FoundationDB does not support transactions running over five seconds. Large transactions Transaction size cannot exceed 10 MB of total written keys and values. Large keys and values Keys cannot exceed 10 kB in size. Values cannot exceed 100 kB in size. == History == FoundationDB, headquartered in Vienna, Virginia, was started in 2009 by Nick Lavezzo, Dave Rosenthal, and Dave Scherer, drawing on their experience in executive and technology roles at their previous company, Visual Sciences. In March 2015 the FoundationDB Community site was updated to state that the company had changed directions and would no longer be offering downloads of its product. The company was acquired by Apple Inc., which was confirmed March 25, 2015. On April 19, 2018, Apple open sourced the software, releasing it under the Apache 2.0 license.
SDL plc
SDL plc was a British multinational professional services company based in Maidenhead, Berkshire, United Kingdom. SDL specialized in language translation software and services (including interpretation services). It was listed on the London Stock Exchange until it was acquired by RWS Group in November 2020. == Name == SDL is an abbreviation for "Software and Documentation Localization". == History == The company was founded by Mark Lancaster with nine employees in 1992. It opened its first overseas office in France in 1996 and was first listed on the London Stock Exchange in 1999. The company grew organically and via acquisitions. SDL acquired Polylang Multimedia in 1998, International Translation & Publishing (ITP) in 2000, Alpnet in 2001, and the machine translation (MT) assets of Transparent Language in 2001. It bought Trados, a rival translation memory (TM) developer, in 2005. In 2007, the company acquired Tridion, a content management system vendor, and PASS Engineering, developers of the Passolo software. In 2008, it bought Idiom Technologies, a global information system management business. In July 2009 SDL acquired XyEnterprise in an all-cash transaction to add XML Professional Publisher as well as Contenta content management software and LiveContent to manage and deliver XML. This unit combined with Trisoft formerly Infoshare. In December 2009, SDL acquired Fredhopper, a Dutch eCommerce onsite search and navigation, onsite targeting and targeted advertising software vendor. Later that same year, it bought Xopus, another Dutch company and the leader in online XML editing. In May 2011 SDL acquired Dutch-based Media Asset Management company, Calamares, in 2012 the campaign management and social media analytics company, Alterian, and in 2013, bemoko, a supplier of internet software for mobile devices. In January 2016, having undertaken a strategic review, SDL announced the divestment of Fredhopper and Alterian as non-complementary to its new strategy. In August 2020 RWS Group announced a proposed takeover of the company for £809 million. The transaction was completed on 4 November 2020. == Operations == SDL provided software for language translation purposes.
Luca Maria Gambardella
Luca Maria Gambardella (born 4 January 1962) is an Italian computer scientist and author. He is the former director of the Dalle Molle Institute for Artificial Intelligence Research in Lugano, in the Ticino canton of Switzerland. He is currently the prorector of Università della Svizzera italiana, where he directs the Master of Science in Artificial Intelligence degree course. Several of his papers have been extensively cited, with his collaborators including Marco Dorigo, with whom he has published papers on the application of ant colony optimization theory to the traveling salesman problem, and Jürgen Schmidhuber with whom he has published research on deep neural networks.. Beside working in research, Gambardella explores the potentials of AI applied for the generation of art. Some of his artistic installations received significant media coverage. As a novelist, the genres he approached broad from Bildungsroman of his first book "Sei vite" ("Six lives"), to romance of his second book "Il suono dell'alba" ("The sound of sunrise").
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EffectsLab Pro
EffectsLab Pro is a discontinued visual effects software product developed by FXhome. It has since been superseded by the FXhome HitFilm range. The company also produced a limited functionality version, EffectsLab Lite, containing just the Particle engine. A more extensive product, VisionLab Studio, combined the functionality of EffectsLab Pro and the company's CompositeLab Pro product with enhancements to both. == Effects Engines == The effects are generated by the program's effect engines: The Neon Light engine allows light beams to be drawn onto the video, allowing the generation of lightsaber-like weapons, neon lighting, fantasy glow effects and laser blasts. The Particle engine is used for particle effects, such as smoke, fire, explosions, and weather effects. The Muzzle Flash engine is designed for creating and animating muzzle flashes such as machine gun firing, tank blasts, etc. It's possible to rotate the created muzzle flash in 3D, making it the only engine with 3D use. The Optics engine is designed for creating artificial lens flares and light sources. It is useful for enhancing other light-based effects, and mimicking the distinctive flashes of light that accompany Star Wars' lightsaber battles. The Laser engine (introduced in EffectsLab Pro in late 2007) is designed as a simplified method of creating laser weapon effects, including the ability to add simulated perspective to the effect. == Presets == EffectsLab Pro allows the user to save the effects using presets. Since all effects are generated from settings in the different engines, it is fairly easy to generate an XML style description of the effect. It is also possible to share presets on FXhome's website.
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